The outcomes of a mobile just-in-time-learning intervention for teaching bioethics in Pakistan
Bibliographic record
Abstract
INTRODUCTION: The study aimed to test the effectiveness and the feasibility of a mobile just-in-time-learning (m-JiTL) approach for teaching bioethics at a university in Pakistan. Over four months, a mobile app (EthAKUL) was used to enhance ethical reasoning among practising nurses, trainee physicians, and medical and nursing students utilising the m-JiTL approach. Participants used EthAKUL to access bioethics modules and participate in asynchronous discussions. METHODS: A mixed methods design was adopted. Pre- and post-knowledge tests were used to assess changes in participants' knowledge of bioethics concepts, while pre- and post-surveys were used to assess changes in participants' attitudes towards m-learning. After the intervention, focus group discussions with the participants were held. Analysis of the discussion posts and meeting notes was conducted. RESULTS: The learners had a favourable attitude toward using mobile devices for learning purposes at the start of the intervention, and the score remained positive afterwards. Bioethics knowledge test scores improved at the end of the intervention, with medical students experiencing the greatest improvement. However, because of the high drop-out rate and lack of participation after the initial phase, it is unclear whether the increase in score or positive attitude is the result of the intervention, making it difficult to draw firm conclusions about the intervention's success. CONCLUSIONS: EthAKUL is the first of its kind app for teaching bioethics, and the study has offered important insights into adopting new pedagogies and technologies for bioethics teaching. It has also identified issues with the design of the app and m-JiTL pedagogy that must be addressed before curriculum-wide adoption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".