Geropsychology content in clinical training programs: a comparison of Australian, Canadian and U.S. data
Bibliographic record
Abstract
BACKGROUND: There is a worldwide shortage of mental health professionals trained in the provision of mental health services to older adults. This shortage in many countries is most acutely felt in the discipline of psychology. Examining training programs in clinical psychology with respect to training content may shed light on ways to increase interest among students and improve practical experiences in working with older adults. METHODS: A large multinational survey of geropsychology content in university-based clinical and counselling psychology training programs was conducted in 2007 in the U.S.A., Australia, and Canada. Both clinical/counseling programs and internship/practicum placements were surveyed as to staffing, didactic content and training opportunities with respect to geropsychology. RESULTS: Survey response rates varied from 15% in the U.S.A. (n = 46), 70% in Australia (n = 25) to 91.5% in Canada (n = 22). The U.S.A. and Australia reported specialist concentrations in geropsychology within graduate clinical psychology training programs. More assessment and psychopathology courses in the three countries were cited as having ageing content than psychotherapy courses. Many non-specialist programs in all three countries offered course work in geropsychology, and many had staff who specialized in working clinically with an older population. Interest in expanding aging courses and placements was cited by several training sites. Recruiting staff and finding appropriate placement opportunities with older adult populations were cited as barriers to expanding geropsychology offerings. CONCLUSIONS: In light of our results, we conclude with a discussion of innovative means of engaging students with ageing content/populations, and suggestions for overcoming staffing and placement shortcomings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".