Faculty development: What do we know about barriers, enablers, and satisfaction levels among African oncology faculty?
Bibliographic record
Abstract
11023 Background: Faculty development (FD) programs and initiatives have been shown to improve teaching, learning, and overall satisfaction levels of academic faculty. However, these benefits are not fully realized in resource constrained settings like those found in some Sub-Saharan African academic institutions, that often face many FD challenges. Improving FD activities in the region may enhance the capacity of oncology faculty to address these challenges. We sought to examine African oncology faculty’s satisfaction and the perceived enablers and barriers with current FD opportunities. Methods: We randomly surveyed oncology faculty ( n = 21) through the African Organization for Research and Training in Cancer (AORTIC) listserv and conducted semi-structured interviews with nine ( n = 9) faculty involved in African oncology training programs to ascertain their perspectives on faculty development activities including curriculum development, teaching, and learning. All survey respondents and interview participants are current members of the AORTIC. Descriptive and inferential statistical techniques, and thematic analysis were used to analyze the survey and interview data respectively. Results: Interim survey results revealed that 64% of academic oncology faculty believe that there are barriers to their FD at their current academic institutions. Barriers cited for FD from the interviews include the competitive nature of FD courses and programs, limited online learning opportunities, poor internet access, time constraints, language barriers, and high costs associated with FD activities. A significant minority of the survey respondents (43%) were dissatisfied with their overall FD. Access to curriculum development opportunities (χ2 = 10.97, p = 0.001) and longer duration of practice (χ2 = 7.9, p = 0.019) were significantly associated with an increased overall satisfaction with FD of oncology faculty. Themes emerging from the interviews also revealed that participants believe that addressing issues relating to access to local institutional support and opportunities including funding, reduced fees for individuals from low- and middle-income countries, getting time off work from local institution, and availability of online FD education will enable them to increase their participation in FD activities. Conclusions: A considerable number of African oncologists face many FD challenges and are therefore dissatisfied with the current state of their FD. Incorporating the recommendations offered by participants into faculty development planning activities may improve faculty satisfaction levels, remove barriers, and improve outcomes for learners. Also, the finding that access to curriculum development opportunities leads to increased levels of satisfaction with FD could guide FD for faculty in African oncology training programs.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".