Accessible continued professional development for maternal mental health
Bibliographic record
Abstract
BACKGROUND: Changing global health and development trends have resulted in a need for continued professional development (CPD) within the health and development sectors. In low-resource settings, where the need for training and CPD may be highest, there are significant challenges for disseminating information and skills. There is a need to improve mental health literacy and reduce levels of stigma about maternal mental illness. The Bettercare series of distance learning books provides a peer-based format for CPD. We aimed to evaluate the Bettercare Maternal Mental Health book as a format for CPD. AIM: The aim of this study was to determine whether the Bettercare Maternal Mental Health book significantly improves knowledge and decreases stigma around mental health for care providers from the health and social development sectors. SETTING: One hundred and forty-one participants (social workers, nursing students and health professionals) were provided with the Bettercare Maternal Mental Health book to study. METHODS: Before and after studying the book, the same multiple-choice knowledge test and the Mental Illness Clinicians' Attitude Scale were used to assess cognitive knowledge and mental health stigma, respectively. RESULTS: Participants' knowledge showed a statistically significant (p < 0.001) improvement between the pre- and post-test results, for all six chapters of the book. However, participants' attitudes towards mental illness did not show a statistically significant change between the pre- and post-test results. CONCLUSION: We found that this method of learning elicited significant improvement in mental health knowledge for care providers. Continued professional development policy planners and curriculum developers may be interested in these findings.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".