Journal of Pharmacy and Pharmaceutical Research Open Access

Ageism in Healthcare and Non-Healthcare Groups of Students toward Older Adults before and During the COVID-19 Pandemic

Sofija Sesto¹, Marina Odalovic^1*, Daniela Fialova^2,3, Martin Henman⁴, Valentina Marinkovic¹ and Ivana Tadic^{1 1}Department of Social Pharmacy and Pharmaceutical Legislation, University of Belgrade, Vojvode Stepe Belgrade, Serbia
²Department of Social and Clinical Pharmacy, Charles University, Prague, Czech Republic
³Department of Geriatrics and Gerontology, Charles University, Prague, Czech Republic
⁴Department of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin D02, Republic of ireland
^*Correspondence: Marina Odalovic, Department of Social Pharmacy and Pharmaceutical Legislation, University of Belgrade, Vojvode Stepe Belgrade, Serbia, Email:

Received: 08-Oct-2022, Manuscript No. IPIPR-22-14502; Editor assigned: 10-Oct-2022, Pre QC No. IPIPR-22-14502 (PQ); Reviewed: 24-Oct-2022, QC No. IPIPR-22-14502; Revised: 27-Jan-2023, Manuscript No. IPIPR-22-14502 (R); Published: 03-Feb-2023, DOI: 10.21767/IPIPR.7.2.007

Introduction

In 1969, Robert Butler introduced the term “ageism” as “a process of systematic stereotyping, prejudicial attitudes and direct or indirect discrimination against people because they are old” [1]. Ageism represents a third “ism” in addition to well-known racism and sexism [2]. Behavior such as avoidance, negative attitudes, and stereotypes about the specific age group of people could also be recognized as ageism. The literature identified four types of ageism: Negative prejudices, negative discrimination, positive prejudices, and positive discrimination. According to Palmore, nine negative stereotypes could characterize negative prejudices against older people: Illness, impotence, ugliness, mental decline, uselessness, isolation, poverty, and depression; and eight positive stereotypes: Kindness, wisdom, reliability, wealth, political power, freedom, eternal youth, and happiness [3]. Ageism can be explicit and implicit. Explicit ageism occurs with conscious awareness, intention, or control in the thoughts, feelings, or actions, while implicit ageism occurs with very little awareness or intention and impacts social interactions [4]. Positive or negative stereotypes perceived by older people could affect people's mental or physical health differently. Levy explained that each ageism predictor could affect health through three pathways psychological, behavioral, and physiological [5,6]. In the past few years, several studies showed the harmful effects of ageism on the health of older people, such as deterioration in the mental health of older people, increasing mortality, loneliness, and a decline in cognitive abilities [7,8]. There are several self-reported instruments published in the scientific literature used for measuring ageism against older people, such as Attitudes toward old persons scale facts on aging quiz, the ageing semantic differential scale, original and short version of the expectations regarding aging questionnaire, attitudes to aging questionnaire and Fraboni Scale of Ageism (FSA). Only FSA possesses multidimensional constructs among all these instruments, as the scale includes domains such as antilocution, discrimination, and avoidance. Antilocution describes negative verbal remarks against a person, group, or community. This scale has also been validated in several countries, namely Canada, USA, France, China, Turkey and Israel. The Fraboni scale was selected as it has been used in previous studies related to Ageism [9]. During the COVID-19 pandemic, "being old" means having more chances of severe infections, deterioration of diseases, increased mortality rate, and poorer health and well-being. In addition, older people were portrayed as vulnerable and frail [10]. The report on older people's position at the beginning of the pandemic indicated isolation of older people and a higher risk of ageist behavior towards them [11]. These conditions could have contributed to anxiety, depression, and fear of loneliness among older people, leading to more healthcare professional visits [12]. However, since the pandemic changed the daily routines of older people, their support, and the availability of healthcare services, ageism has been seen during the provision of healthcare to older people in different settings of care as well [13]. There are few reports documenting older people’s experiences with healthcare institutions, but research on age related inequities in providing healthcare services due to ageism attitudes among healthcare professionals is scarce [14]. Given the possibly detrimental effects of ageism on individuals and societies, we were interested in healthcare students perception of ageism as future healthcare professionals in the crisis during the COVID-19 pandemic. Several previous studies on the student population examined the level of ageism and ageist behavior towards older adults before the pandemic. These studies predominantly included the population of healthcare students in different disciplines (medical, nursing, dentistry, pharmacy and special education and rehabilitation students. A complete spectrum of ageist attitudes towards older adults has been reported, from positive, slightly positive, neutral, and negative to very negative. Some studies determined that the education and practical experience of medical students accompanying the care of older people may contribute to less ageist attitudes. One study among physicians, nurses, and social workers revealed a certain level of ageist attitudes of these professionals during healthcare provision [15].

The evidence of ageism levels in the population of students of different scientific fields during the COVID-19 pandemic is missing. Healthcare students are expected to have less ageist thoughts, attitudes, and behaviors against older people than non-healthcare students. The healthcare students, as future healthcare professionals, are expected to provide healthcare to all patients regardless of their age (40,41,43,44,47,50,54). The provision of compassionate care with respect for human needs and values, without prejudice and discrimination and in line with codes of ethics, is expected to be developed, especially during healthcare studies. In addition, empathy and feeling to help frail older adults, especially during the COVID-19 pandemic, should have lower levels of ageism in healthcare professionals and students. Therefore, we intended to examine the level of ageism toward older adults among healthcare and non-healthcare students before and during the COVID-19 pandemic using the FSA, and to consider if predictors for a higher level of ageism exist between HCS and non-HSC and in the entire population.

Materials and Methods

The cross-sectional study was conducted in May 2020, using an online, anonymous survey to collect student’s data from four public universities in four different regions of the Republic of Serbia. The survey was created using Google forms. Official emailing lists of students and students social media groups were used as dissemination channels (targeted population for dissemination: About 15000 students of 4 participating universities). The study was approved by the ethics committee (protocol No.1452/2) of the university of Belgrade faculty of pharmacy, Republic of Serbia. Collected data were processed anonymously, and the study fully respected all ethical committee rules. The principal investigator sent the call for participation to potential participants only once. Participants were asked to express informed consent to participate in the first part of the questionnaire before answering the questions.

After calculation of study power, a minimum sample size of 375 students has been estimated for the population of 15000 students actively enrolled in the four participating universities considering the confidence level of 95% and marginal error of 5% [16].

Participants

The population of participating students was divided into two groups: Health Care students (HC), which enrolled in medical, dental, and pharmacy study programs and non-Healthcare (non-HC) students, that enrolled in philosophy, electronics, economy, law, technology, security, and other study programs. The inclusion criteria were as follows: Enrollment in the faculty of four participating universities, an open email account or account of social media channels that students use for academic correspondence, and sharing academic information. Incomplete responses have been excluded from the analysis.

Online Survey

The online survey included 39 items divided into two sections: 1) a general section of 10 items to examine participants sociodemographic and lifestyle characteristics, including interactions with older people before and during the COVID-19 pandemic and 2) FSA, which consists of 29 items.

Items in the first section had multiple choice options. They included items related to gender, name of university and study programs, year of study, the status of living with older people (before and during the COVID-19 pandemic), and provision of help to older people (providing food, medications, products for hygiene, disinfectants, before and during the COVID-19 pandemic). An item about participants age was with a short answer option. In addition, participants were asked whether they agreed with two statements regarding ageism in the provision of healthcare (every person, regardless of their age, deserves equal access to healthcare and every person, regarding its age, deserves equal respect).

The second section included 29 items of FSA. Each item of FSA was offered in two forms concerning two different time frames: Before and during the COVID pandemic. Therefore, students expressed their previous and current attitudes toward older people on one occasion. According to the original study of the development of the FSA scale, this scale possessed good internal consistency (Cronbach’s α (α) was 0.86 [17]. Each item in this scale is rated on a 4-point scale (from 1-strongly disagree to 4-strongly agree, without a neutral response option). Seven items were reverse scored (items no: 8,12,14,21,22,23, and 24). All items were grouped into three domains: 1) Antilocution (items no: 1,3,4,5,9,16,25,27,28,29) with final scoring from 10 to 40; 2) Discrimination (items no: 2,8,17,18,20,21,22,23,24) with final scoring from 9 to 36; and 3) Avoidance (items no: 6,7,10,11,12,13,14,15,19,26) with final scoring from 10 to 40. The total FSA score was calculated as a sum of scores of all items/domains, and this total score could be in the range from 29 to 116, where higher scores indicated more ageism [18]. Permission to use the FSA scale was obtained from the copyright holder and creator of the scale.

Translation and Cultural Adaptation of the FSA

Translation and the cultural adaptation of the FSA scale were made following the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) guidelines [19].

Translation of the original FSA scale was performed by three native Serbian speakers (including two authors of this study SS and IT, and a healthcare professional). All of them did independent translations from English to Serbian. Later, they established a consensus on the translations and prepared a version to be back translated into English. Separately, two other professional, bilingual translators did independent translations from Serbian into English. The leading researchers (SS and IT) compared all translated versions (forward and backward translations) and agreed on a consensus about Serbian's pre-final FSA scale version. Later on, cognitive debriefing was performed with a small number of students (n=8), to pre-test the pre-final FSA scale version. In this phase, each student was asked to comment on each FSA scale item's simplicity, clarity, and relevance. Cognitive debriefing revealed that ten items required slight changes to sound more like the native Serbian language (for example, change in the order of words in a sentence).

Statistical Analysis

All data were analyzed separately for the total sample and the students groups (healthcare and non-healthcare students). Descriptive statistical analyses were applied to summarize the participants main sociodemographic characteristics and included calculation of prevalences, mean values, median, Standard Deviation (SD), range, and distribution of missing data. The type of distribution of the variables was tested using the Kolmogorov-Smirnov test. The differences between the two groups of participants were tested using the Mann-Whitney U test. Differences in measurements (variables referred to before and during the pandemic) within each group of participants were tested using the Wilcoxon signed-rank test. The relation of categorical data was tested using the Chi-square test. Analysis of FSA scale included calculation of mean values and SDs for each domain's score, the total FSA score, and the internal consistency test (α coefficient and α if each item is deleted). The marginal value of α for good internal consistency was set at 0.7 (57). The cut off value of FSA total score has been determined in a Turkish study in a sampling of healthcare workers (the cut off value was set as 78) [20]. As our median value of total Fraboni score for the whole sample was much below this value, and we performed a study on a sample of students, we calculated the statistical cut off score using the Receiver Operating Characteristic (ROC) curve.

We have used the item "every person, regarding his age, deserves equal respect" as an anchor question. This item reflects an equal attitude to people regarding age without discrimination. This item was rated on a 4-grade scale (from 1-extremely disagree to 4-extremely agree). The scoring of this item was then reversed as the FSA score was also reversed. For ROC analysis answers “extremely disagree” and “disagree” was grouped as a category of 1, and "extremely agree" and "agree" as a category of 0. We have applied linear regression analysis to check the correlation between this anchor question and FSA total score.

The ROC curve was presented as a graphical plot of specificity (false positive rate) and Sensitivity (true positive rate) at various threshold settings of FSA scores. The curve was dichotomized according to anchor questions groups of answers. The cut off value was determined by locating a point in the ROC curve that has the nearest to the maximum value of sensitivity. Students with total FSA scores above cut off values were considered to demonstrate a higher level of ageism toward older people. Binary logistic regression was used to establish the relationship between the FSA total score as a dependent variable (the reference category: Level ≤ cut off) and other independent variables (female/male gender, healthcare/non-healthcare students, the status of living with/without older people and provision of help/no help to older people). The Hosmer-Lemeshow test was used to measure the model's goodness of it (values of p<0.05 meant that the model was of poor it) (59). Statistical analyses were performed using the Predictive Analytics So tware, version 28 (Armonk, NY: IBM corp). The level of signi icance has been determined by the value of p<0.05.

Results

In total, 505 university students were accepted to participate in the survey. Due to incomplete answering of all items, 3 (0.6%) students were excluded from the data analysis. The analysis included results of 502 students (314 (62.5%) healthcare and 188 (37.5%) non-healthcare). Participant’s sociodemographic characteristics are presented in Table 1. Statistically significant differences between students groups were noted for gender and duration of studies.

Table 1: Students sociodemographic characteristics.

Internal consistency (values of α) of the total FSA scale for the whole sample of students before and during the pandemic was above 0.7, as opposed to the antilocution and discrimination domains which had α <0.7. Values of α if item deleted for the whole scale were in the range 0.83-0.86, and for domains in the range 0.48-0.75 (Table 2).

Table 2: Internal consistency of the FSA scale.

The mean values of FSA scores in a group of all students were 62.47 before the pandemic (median 63.00, range: 36-92) and 64.33 during the pandemic (median: 64.00, range: 36-98). There were statistically significant differences in FSA total scores and FSA domains for both groups of students before and during the pandemic (Table 3). The difference in FSA scores between HCS and non-HCS was not statistically significant for the period before (U total=26,896.5, p>0.05) and during the pandemic (U total=26,826.5, p>0.05).

The mean values of FSA scores related to the type of study (healthcare vs. non-healthcare students) were 61.84 vs. 63.53 before the pandemic (median 62.00 vs. 64.00) and 63.58 vs. 65.59 during the pandemic (median values equal in both groups, 64.00). In all observed groups, the mean values for scores before the pandemic were lower than during the pandemic, indicating more potent ageist thoughts and attitudes during the pandemic (Table 3).

Table 3: FSA scores before and during the pandemic.

There were no statistically significant differences between female and male students in total FSA score and its domains: Antilocution, discrimination and avoidance before pandemic (U_total=14302.00, U_antilocution=14345.00, U_{discrimination}=15110.00, U_avoidance=14907.50 respectively) and during pandemic (U_total=14622.50, U_antilocution=14941.50, U_{discrimination}=14947.00, U_avoidance=15172.00 respectively). Between values of anchor question and FSA total scores, the correlation was not strong (r=0.261) but statistically significant (p<0.001) (Figure 1).

Figure 1: ROC curve analysis with optimal FSA cut off value.

The ROC curve based on this dichotomization of the anchor question is shown in Figure 1. The Area Under the Curve (AUC) was 0.63 showing poor classification accuracy (p=0.68). The point for sensitivity=0.56 and specificity=0.46 was the nearest to the maximum value of sensitivity, and the statistical cut off score was determined to be 63.5.

Predictors of ageism before and during the COVID-19 pandemic were estimated as significant for the status of helping people over 65 years before the. That meant that students who were not helping people over 65 had 47%higher odds of having the highest ageist attitudes before the pandemic (and 42% respectively for the period during the pandemic). Other factor variables did not influence ageist attitudes before and during the pandemic. Predictors of ageism were not statistically significant within the groups of HCS and non-HCS (Table 4).

Table 4: Predictors of ageism before and during COVID-19 pandemic.

Discussion

This study is the first in Serbia to examine ageist attitudes before and during the pandemic in healthcare and non-healthcare students. Our study revealed that both groups of students had less ageist attitudes before the pandemic, but statistically significant differences between HCS and non-HCS were not confirmed.

that positive attitudes and quality of relationships with older people were linked to better connections with them (for example, if they were grandparents or family friends). In this study, over 30% of healthcare and non-healthcare students lived with people over 65 years before and during the pandemic, so these relationships between students and older people in their households can be the reasons for less ageist attitudes in general.

These relationships between the younger and older population affected a predictor of ageism “providing help to older people before/during pandemic” to be the only one significant. Students with more empathy towards the older people (willing to help, visit, and spend some time with the older people) had less chance of showing negative attitudes and less ageism during the pandemic. In general, participants probably considered their grandparents very important in their lives. Similar findings were also shown in the Spanish study of Muntsant, et al. in the student population, whichshowed that helping older people or grandparents reduces unconscious ageism.

In addition, knowledge of aging reduces ageist attitudes. Medical students who attended a geriatric course showed almost the same total FSA score in the study of Lee, et al., as our healthcare students before the pandemic, although students who did not attend geriatric courses showed higher scores and more ageist attitudes. Therefore, organizing placements or volunteering in helping to older people could positively influence student’s attitudes toward older people at the start of their professional careers.

During the COVID-19 pandemic, ageism increased. Media and policy briefs during the early phase of the COVID-19 pandemic and age specific quarantine measures suggest that ageist attitudes exist and are being shared through public forums. Older people experienced age based discrimination and ageist attitudes during the first quarantine period in 2020. As shown in this research, more ageist attitudes of students during the COVID-19 pandemic could be influenced by special measures and changes for older people in Serbia. These measures made older people more isolated and discriminated.

Several studies have revealed that young people present more negative ageist attitudes than older people, and that men were more likely to avoid and have negative stereotypical ageist attitudes than women. However, in this study, there is no difference in ageism attitudes related to study groups of students or their gender.

Conclusion

During the pandemic, the scores of ageism were higher in both groups of HCS and non-HCS students. However, the differences between groups were not significant. The only statistically significant predictor associated with the high level of ageism was the non-provision of help to older people before/during the pandemic. These findings indicate that the active involvement of students of various HC and non-HC disciplines in care for older adults might significantly reduce ageist attitudes, thoughts, and prejudices. Better involvement of older patients in social interactions with the younger generation may increase intergenerational solidarity.

Acknowledgments

This study was inspired by the Euro Ageism H2020 project held in several EU countries during 2017-2022, and we would like to thank the researchers of this European project for such inspiration. We want to thank also to translators and the survey participants for their contribution to this paper.

Conflict of Interest

The authors declare that there is no conflict of interest.

References

1. Butler RN (1969) Ageism: Another form of bigotry. The Gerontologist. 9(4):243-246.

   [Crossref] [Google Scholar] [PubMed]

2. Butler RN (1995) The Encyclopedia of Aging. 4^th edition. Springer Publishing Company, Springer-Verlag Berlin Heidelberg, Newyork, USA. 1323.

   [Google Scholar]

3. Palmore E (1999) Ageism: Negative and Positive. 2^nd edition. Springer Publishing Company, Springer-Verlag Berlin Heidelberg, Newyork, USA. 288.

   [Google Scholar]

4. Levy B, Banaji M, Nelson T (2017) Implicit ageism. In Ageism: Stereotyping and Prejudice against Older Persons. 2^nd edition, The MIT Press, Cambridge, Massachusetts, USA.

   [Crossref]

5. Levy B (2009) Stereotype Embodiment: A Psychosocial Approach to Aging. Curr Dir Psychol Sci. 18(6):332-336.

   [Crossref] [Google Scholar] [PubMed]

6. Lyons A, Alba B, Heywood W, Fileborn B, Minichiello V, et al. (2018) Experiences of ageism and the mental health of older adults. Aging Ment Health. 22(11):1456-1464.

   [Crossref] [Google Scholar] [PubMed]

7. Allen JO (2016) Ageism as a Risk Factor for Chronic Disease. Gerontologist 56(4):610-4.

   [Crossref] [Google Scholar] [PubMed]

8. Barber SJ (2017) An examination of Age based stereotype threat about cognitive decline. Perspect Psychol Sci. 12(1):62-90.

   [Google Scholar] [PubMed]

9. Kogan N (1961) Attitudes toward old people: The development of a scale and an examination of correlates. J Abnorm Soc Psychol. 62:44-54.

   [Google Scholar] [PubMed]

10. Palmore E (1977) Facts on aging. A short quiz. Gerontologist. 17(4):315-320.

    [Crossref] [Google Scholar] [PubMed]

11. Rosencranz HA, McNevin TE (1969). A factor analysis of attitudes toward the aged. Gerontologist. 9(1):55-59.

    [Crossref] [Google Scholar] [PubMed]

12. Ayalon L, Dolberg P, Mikulioniene S, Bialas JP, Rapoliene G (2019) A systematic review of existing ageism scales. Ageing Res Rev. 54:100919.

    [Crossref] [Google Scholar] [PubMed]

13. Sarkisian CA, Hays RD, Berry S, Mangione CM (2002) Development, reliability, and validity of the expectations regarding aging (ERA-38) survey. Gerontologist. 42(4):534-542.

    [Crossref] [Google Scholar] [PubMed]

14. Sarkisian CA, Steers WN, Hays RD, Mangione CM (2005) Development of the 12-item expectations regarding aging survey. Gerontologist. 45(2):240-248.

    [Crossref] [Google Scholar] [PubMed]

15. Laidlaw K, Power MJ, Schmidt S, Group WO (2007) The Attitudes to Ageing Questionnaire (AAQ): Development and psychometric properties. Int J Geriatr Psychiatry. 22(4):367-379.

    [Crossref] [Google Scholar] [PubMed]

16. Fraboni M, Saltstone R, Hughes S (1990) The Fraboni Scale of Ageism (FSA): An attempt at a more precise measure of ageism. Can J Aging. 9(1):56-66.

    [Google Scholar]

17. Rupp DE, Vodanovich SJ, Crede M (2005) The multidimensional nature of ageism: Construct validity and group differences. J Soc Psychol. 145(3):335-362.

    [Crossref] [Google Scholar] [PubMed]

18. Boudjemad V, Gana K (2009) Ageism: Adaptation of the fraboni of ageism scale revised to the French language and testing the effects of empathy, social dominance orientation and dogmatism on ageism. Can J Aging. 8(4):371-89.

    [Crossref] [Google Scholar] [PubMed]

19. Fan JY, Zhao HM, Liu YT, Kong LL, Mao J (2020) Psychometric properties of a Chinese version of the fraboni scale of ageism: evidence from medical students sample. BMC Med Educ. 20(1):197.

    [Crossref] [Google Scholar] [PubMed]

20. Kutlu Y, Kucuk L, Yildiz Findik U (2012) Psychometric properties of the Turkish version of the fraboni scale of ageism. Nurs Health Sci. 14(4):464-471.

    [Crossref] [Google Scholar] [PubMed]

Limitations

The internal consistency of the total FSA scale was of high value. The internal consistency values of several domains (antilocution and discrimination before and during the pandemic) and consistency if items were deleted were below the reference value.

That leads to the conclusion that some FSA items do not entirely fit with specific domains but fit well into the overall FSA scale. Other researchers have previously reported similar low α values of antilocution and discrimination domains. One reason for these lower α values could be that some items could belong to two different domains or be classified in different domains. As α of the total FSA scale is high above the reference value, we may conclude that this scale possesses strong consistency and is reliable inmeasuring ageism levels. This study possesses several limitations: The study was conducted in May 2020 when the strict lockdown measures for older people were canceled in Serbia. As the survey was conducted at one time period and one questionnaire examined student’s attitudes before and during the pandemic, our study may possess some recall bias. As the study was conducted in the first year of the pandemic and the research was conducted in a younger population, this bias could have a lower impact on the collected results. We may also expect that with the prolonged duration of the pandemic period, negative ageist attitudes might significantly increase, and differences could be even higher.