Geropsychology career pipeline perceptions
Bibliographic record
Abstract
OBJECTIVE: Using the framework of Social Cognitive Career Theory, this study aimed to ascertain attitudes and perceptions of geropsychology career paths, given the present notable geriatric workforce shortage. METHODS: An online survey was developed iteratively and disseminated through various modalities (i.e., internet, email, word-of-mouth). Participants included 28 predoctoral and 76 professional geropsychologists (N = 107; age M = 39.18, SD = 12.05). The sample was largely female (72%), non-Hispanic White (89%), and has or was working towards their PhD (82%). RESULTS: Results delineate attractive and unattractive aspects of common career options (academic, clinical Veterans Affairs [VA], clinical non-VA), and assessed the hypothetical proclivity and feasibility of switching between academic and clinically focused careers. The results found gender (women vs. men) and career stages (predoctoral vs. professional) to be significant contributors to career perceptions. CONCLUSIONS: The present study advances past literature by unveiling potential avenues to ameliorate this workforce shortage within both clinical and academic fields in geropsychology.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".