Exploring Recruitment Strategies to Hire Occupational Therapists
Bibliographic record
Abstract
BACKGROUND: Recruitment issues in occupational therapy have been a long-standing concern for the profession. PURPOSE: This descriptive study explored the strategies currently being used by employers to recruit occupational therapists for employment purposes. METHOD: An 18-item survey was mailed to 251 sites where occupational therapists work in Alberta and Saskatchewan. RESULTS: There was a 64% response rate and data from 130 surveys were analyzed. The results indicate that employers continue to rely on a wide variety of strategies for advertising and recruiting, the most prevalent being word of mouth, postings at universities, and providing student fieldwork placements. In turn, the most effective recruitment strategies were listed as word of mouth, advertising in the general media, and providing student fieldwork placements. Various examples of financial incentives offered by employers were also listed. Many participants identified recent changes in recruitment strategies such as making a move towards web site job postings. PRACTICE IMPLICATIONS. The results suggest strategies for employers to target for recruiting occupational therapists and illustrate to both employers and students the importance of fieldwork in recruitment and hiring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".