Enablers & barriers to online and e-learning for health professionals in Pakistan: a formative research
Bibliographic record
Abstract
OBJECTIVE: To design a continuous professional development programme pertaining to integrated nutrition, health, and Water, Sanitation and Hygiene interventions for health professionals (HP). Methods: The cross-sectional study was conducted from January 1 to April 30, 2024, in Lahore, Pakistan, after approval from the ethics review committee of the Institute of Clinical Psychology, University of Management and Technology, Lahore. The sample was raised from among students of either gender aged 18-26 years studying at various public and private universities in Lahore. Data was collected using the Attachment Styles Scale, Dating Violence Scale, Self-Criticism Scale and the Psychosocial and Emotional Reactions Scale. Data was analysed using SPSS 25. RESULTS: Of the 1,768 publications identified, 41(2.3%) were analysed in detail. Of the 15 key informants interviewed, 7(46.6%) were HPs, while 4(26.7%) each were police-makers and facilitators or designers of online modules. Under the two broad categories of "enablers" and "barriers", five subthemes emerged for each. The subthemes related to enablers included personal, content and design, quality of facilitators, peer connectivity, and social networking. The subthemes related to barriers were structural, cultural, curricular and content, human resources, and financial. Conclusion: In Pakistan, the corona virus disease-2019 pandemic brought about a forced digital transformation for health professions students. Blended learning emerged as an effective e-learning model. To inform the development, implementation and sustainability of online continuous professional development of health professionals, a guiding framework comprising various elements, like financing mechanism, appropriate materials and activities, communication strategy, and an evaluation process, incorporating regulatory perspective, contextual factors and clear objectives is essential.
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".