Mobile-based blended learning for capacity building of health providers in rural Afghanistan
Bibliographic record
Abstract
BACKGROUND: Mobile-based blended learning initiative was launched in November 2014 in Badakshan province of Afghanistan by Tech4Life Enterprises, Aga Khan Health Service, Afghanistan (AKHS, A), and the University of Calgary, Canada. The goal of this initiative was to improve knowledge of health providers related to four major mental health problems, namely depression, psychosis, post-traumatic stress disorder (PTSD) and drug abuse. METHODS: This paper presents the results of quasi-experimental study conducted in 4 intervention districts in Badakshan for improvement in the knowledge among health providers about depression. The results were compared with three control districts for the change in knowledge scores. RESULTS: Sixty-two health providers completed pre and post module questionnaires from case district, while 31 health providers did so from the control sites. Significant change was noticed in the case districts, where overall knowledge scores changed from 45% in pre-intervention test to 63% in post-intervention test. Overall background knowledge of pre to post module test scores changed from 30% to 40%, knowledge of symptoms showed correct responses raised from 25% to 44%, knowledge related to causes of depression from overall districts showed change from 22% to 51%, and treatment knowledge of depression improved from 29% to 35%. Average gain in scores among cases was 16.06, compared to 6.8 in controls. CONCLUSIONS: The study confirms that a blended Learning approach with multiple learning techniques for health providers in Badakshan, Afghanistan, enhanced their knowledge and offers an effective solution to overcome challenges in continuing education. Further research is needed to confirm that the gains in knowledge reported here translate into better practice and improved mental health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".