A WhatsApp community forum for improving critical thinking and practice skills of mental health providers in a conflict zone
Bibliographic record
Abstract
A violent conflict, known as the Anglophone Crisis has been occuring in the Northwest and Southwest Regions of Cameroon since 2016. This conflict and associated consequences have affected the way healthcare is provided. To help meet the needs of healthcare workers and other service providers, a community of practice called 'The Forum' was established using WhatsApp Messenger. This mobile learning group aimed to support, equip, and encourage practitioners to engage in critical thinking skills, enabling them to incorporate ongoing learning into their practice. A qualitative phenomenological approach was used to evaluate the experiences of 13 Forum participants through in-depth individual interviews. Four themes were identified: (1) interactive learning to enhance critical thinking; (2) self-regulated learning strategies; (3) WhatsApp as an effective platform to support critical thinking and learning in a conflict zone; and (4) application to practice. This study shows that through participating in The Forum, users engaged in critical thinking on various mental health topics and applied new skills to their professional practice. Impacts of this study include practical implications with recommendations for those looking to develop a collaborative learning community in similar conditions, as well as theoretical contributions.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Qualitative evaluation of a WhatsApp community of practice supporting mental health providers' critical thinking in a conflict zone; the object is professional continuing education, not research practice.
The study evaluates a WhatsApp learning forum for mental-health providers rather than research practice.
Evaluation of a WhatsApp community of practice for mental-health providers’ skills; clinical training, not research workforce.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".