Employment Decisions of Newly Graduated Occupational Therapists
Bibliographic record
Abstract
Introduction. Employment decision-making is essential for understanding workforce trends. Current occupational therapy workforce research describes distribution disparities of occupational therapists within geographic locations, services such as acute care or community health, and private or public sectors. New graduates of occupational therapy programs are critical to meeting the demand and distribution disparities of occupational therapy services in British Columbia. However, recent employment decision-making of new occupational therapy graduates has not been well studied. Purpose. This study aimed to examine factors that influence newly graduated occupational therapists’ employment decisions. Methods. This descriptive study sampled 122 occupational therapists who were registered in one province and graduated from a Canadian occupational therapy program between 2017 and 2022. Data was collected through an online survey about intrinsic factors, extrinsic factors, and past fieldwork experiences that affected participants’ employment decision-making. Descriptive data analysis was used to organize participants’ responses. Findings. Results identified that work-life balance and mentorship were the highest rated factors that influenced participants’ current and first employment respectively. Participants agreed that the variety and number of placements they had as students were more influential to their employment decisions than the length of the fieldwork education. Conclusion. This study identified the intrinsic and extrinsic factors in employment choices that may influence recruitment, retention, and workplace planning of new graduates.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".