Contexts, mechanisms and outcomes of interventions to counter ageism toward older adults in undergraduate health and social services students: Results of a realist review
Bibliographic record
Abstract
Ageism might reduce the effectiveness and quality of care in older adults essential with the aging population. Although interventions to counter ageism were carried out among students, no clear integration of their results described the mechanisms underlying their impact. This study aimed to provide a comprehensive understanding of which interventions amongst undergraduate health and social services students are effective, under which circumstances, how, and with what outcomes. A realist review was carried out using 44 keywords in 6 databases. The configurations for contexts, mechanisms, and outcomes were identified for three types of interventions, considering their impact on stereotypes, prejudices, and discrimination toward older adults. In the 63 studies selected, intergenerational contact interventions exhibited the greatest proportion (14/15; 93.3%) of evidence supporting improvement in outcomes of ageism, followed by combined (67/84; 79.8%) and educational (51/67; 76.1%) interventions. Stereotypes and prejudices were challenged by the transmission of realistic and balanced information, as well as through meaningful and high-quality intergenerational contact. Meeting or hearing about a broad variety of older adults in specific conditions also helped to decrease ageism. Results highlighted the importance of seeing diversity and uniqueness in older adults, and their competencies rather than their limitations. Mechanisms were occasionally hampered by obstacles, such as induced anxiety and confirmation of negative aspects of aging. Countering ageism in health and social services students is essential to ensuring high-quality care. In knowing that educators, professionals, and institutions have a direct influence on students, their awareness of ageism should be enhanced by interventions targeting them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".