Exploring faculty development initiatives in medical education in resource-limited settings: perspectives and challenges
Bibliographic record
Abstract
BACKGROUND: Faculty Development Programs (FDPs) are integral to institutional priorities to support staff members in leveraging the skills necessary to deliver quality education and enhance the overall learning experience. Little is known about their impact in resource-limited settings. Therefore, the objective of this study was to evaluate the perceptions of medical and health faculty members in Sudan toward FDPs by exploring their views on their performance, the learning environment, and the challenges hindering program implementation. METHOD: A descriptive, cross-sectional survey consisting of twenty-six items was used to collect data from faculty members to assess their perceptions of the FDPs. RESULT: There was a 77% response rate (n = 103) to the survey from the targeted sample size of 134. Most of the staff members (90.3%, n = 93) perceive FDP activities as beneficial for enhancing their teaching abilities, while 70.9% (n = 73) see improvement in research practices, and 54.4% (n = 56) observe benefits to their clinical skills. Fewer respondents (46.6%, n = 48) reported improvements in their scientific publications. However, several challenges were identified, with time constraints perceived as a major obstacle to effective program implementation. CONCLUSION: In a resource-limited setting, evaluating the program's effectiveness plays a pivotal role in improving its activities. Providing additional resources, enhancing institutional support, and improving accessibility to activities can strengthen the program's success, ultimately benefiting both staff and students. These insights may offer valuable guidance for institutions facing similar constraints.
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".