Recruitment and Retention of Part-time Clinical Instructors
Bibliographic record
Abstract
The purpose of this project was to explore factors influencing the recruitment and retention of part-time clinical instructors at a community college in Ontario. Interviews were conducted with two project managers and four previously employed part-time clinical instructors. The focus of the analysis was to examine what attracted the clinical instructors to teaching, their journey of teaching, recruitment, and their reasons for leaving. Major themes for leaving centred on workload; student, clinical agency, and personal/professional demands; and remuneration with respect to time spent. Essential factors highlighted for retention included support and guidance to contribute to the success of the clinical instructors’ teaching experience. In the final analysis, five recommendations were proposed: \n1.\tProvide ongoing collegial support and guidance through mentoring. \n2.\tDecrease the instructors’ workload by reducing the number of written assignments and streamlining evaluation methods. \n3.\tExpand the orientation time for new instructors. \n4.\tProvide appropriate remuneration for and recognition of teaching contributions. \n5.\tSupport instructors in addressing their clinical agency concerns.
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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".