Exploring Professional Development Needs of Educators in the Health Sciences Professions
Bibliographic record
Abstract
An increasing number of institutions of higher education are clustering their health sciences schools into a common unit. Therefore, it is imperative that the individual faculty development units assume new mandates to meet faculty development needs for stakeholders across these disciplines. Critical to providing current and relevant professional development activities is an awareness of the needs of academicians, including common as well as discipline-specific needs. Hence, the aim of this study was to explore the extent to which factors such as discipline, rank, gender, education, and years as an academician impact on perceived needs for faculty development. In February 2012, a cross-sectional survey of the perceived faculty development needs of academicians in the health sciences unit of a Canadian university was conducted using an online assessment tool. A total of 133 out of 1,409 potential participants completed the survey, for a response rate of 9.4%. The findings revealed more similarities than differences in terms of perceived faculty development needs. In addition, differences were found across all health professions schools and in factors such as discipline, academic rank, education, gender, and years as an academician. These findings suggest that faculty development and educational specialists should understand the shared as well as the unique needs of the individual health sciences schools in planning their professional development services.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".