The Impact of Mental Health Stigma and Ageism on Students’ Intention to Work with Older Adults: A Mixed Methods Design
Bibliographic record
Abstract
Abstract Approximately 20% of older adults have a mental or neurological disorder which can cause significant disability. With a growing older adult population, there is a need for providers receiving specialized training in aging to provide quality care. However, there continues to be shortages of students seeking careers in geriatrics and especially in working with older individuals with mental health (MH) concerns. The present study explored the relationship between MH stigma, ageism and intention to work with older adults among undergraduate students. Undergraduate students (N=188) completed a battery of questionnaires including intention to work with older adults, positive and negative attitude towards older adults, and open-ended questions exploring MH stigma views. Regression results indicated that MH stigma, positive, and negative attitudes significantly predicted intention to work with older adults, (F(3, 182) = 8.51, p = .000). Examination of the coefficients revealed that positive attitudes significantly predicted intention to work with older adults (t=4.38, p=.000), and MH stigma demonstrated a trend towards significance (t=1.90, p=.059). Open-ended responses were analyzed using qualitative description methods which revealed themes consistent with negative and positive stereotypes, MH problems going undetected, and need for additional support in recognizing and treating MH conditions among older adults. Positive attitudes are an important predictor in students’ intention to work with older adults, and MH stigma may be an important factor to explore further. Qualitative themes also describe how MH concerns are an important area to focus on among older adults, although there continues to be evidence of aging stereotypes.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".