A Canadian faculty of medicine and dentistry’s survey of career development needs
Bibliographic record
Abstract
INTRODUCTION: Career development efforts for adult learners are more likely to succeed if they are responsive to the learners' needs, offered at convenient times, and provided in a variety of forms. METHODS: The Faculty of Medicine and Dentistry at the University of Alberta conducted a self-administered career development needs survey of full-time academic faculty. Faculty members were asked to identify how useful each of 35 career development skills would be to them and at which stage in an academic career it would be most useful. Preferred educational modes and times of delivery were also assessed. The mean rankings of skill needs were examined overall as well as by gender, academic rank, departmental category (clinician versus basic scientist), marital status, and the presence of school-aged children at home. RESULTS: 185 of 446 (41%) mailings were returned, of which 181 were evaluated. The number one perceived career development need across all subgroups was 'Effective writing of grants and publications'. Six of the top 10 needs were identified as useful at all career stages. Significant differences in rankings of needs were noted between subgroups. Half days were preferred to full days for career development efforts and short courses to other educational modes. DISCUSSION: Several career development skills were identified as priorities independently of gender, academic rank, and clinical versus basic science career tracks. Differences suggest that within a basic global programme, a variety of career development modules may need to be developed for subgroups.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".