Problems and issues in implementing innovative curriculum in the developing countries: the Pakistani experience
Bibliographic record
Abstract
BACKGROUND: The Government of Pakistan identified 4 medical Colleges for introduction of COME, one from each province. Curriculum was prepared by the faculty of these colleges and launched in 2001 and despite concerted efforts could not be implemented. The purpose of this research was to identify the reasons for delay in implementation of the COME curriculum and to assess the understanding of the stakeholders about COME. METHODS: Mixed methods study design was used for data collection. In-depth interviews, mail-in survey questionnaire, and focus group discussions were held with the representatives of federal and provincial governments, Principals of medical colleges, faculty and students of the designated colleges. Rigor was ensured through independent coding and triangulation of data. RESULTS: The reasons for delay in implementation differed amongst the policy makers and faculty and included thematic issues at the institutional, programmatic and curricular level. Majority (92% of the faculty) felt that COME curriculum couldn't be implemented without adequate infrastructure. The administrators were willing to provide financial assistance, political support and better coordination and felt that COME could improve the overall health system of the country whereas the faculty did not agree to it. CONCLUSION: The paper discusses the reasons of delay based on findings and identifies the strategies for curriculum change in established institutions. The key issues identified in our study included frequent transfer of faculty of the designated colleges and perceived lack of: Continuation at the policy making level. Communication between the stakeholders. Effective leadership.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".