Increasing Support and Job Satisfaction for Program Administrators at the Postgraduate Medical Education Program at the University of Ottawa: The Program Administrator’s Perspective
Bibliographic record
Abstract
Abstract . Background : Realizing Program Administrators (PAs) are crucial to the success of the postgraduate medical education (PGME) program, the PGME office at the University of Ottawa conducted a needs analysis to (a) identify training opportunities PAs felt would support them in being effective at meeting role expectations including supporting Program Directors (PDs) and (b) gather information from PAs to guide the PGME office in taking positive action toward increasing satisfaction with services and resources. Methods: A mixed methods approach involved collecting and analyzing data from online surveys and follow-up qualitative interviews. Data analysis was conducted using the constructs of the W(e)Learn framework (content, media (delivery), service, structure and outcomes). Results : PAs identified the following professional development topics they said would benefit them: Human Resources; Communication and Conflict Management Courses; Career Development; Evaluation; Policy; Multigenerational Workforces; and Best Technological Practices of Relevance to PAs . The PAs also identified several recommendations for how the PGME office could facilitate them effectively carrying out their roles and responsibilities. Conclusions: An effective form of support is offering convenient, relevant professional development to help employees meet role expectations. A well-designed professional development program should begin with a needs analysis to determine stakeholder needs with regard to relevant content, preferred delivery methods, service issues and course structure, in order to ensure desired learner outcomes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".