Transformative learning in the setting of religious healers: A case study of consultative mental health workshops with religious healers, Ethiopia
Bibliographic record
Abstract
Objective: Psychiatric interventions that consider the socio-cultural and spiritual traditions of patients are needed to address stigma and improve access to mental health services. Productive collaboration between traditional healers and biomedical practitioners hold promise in such efforts, and applying tenets of transformative learning hold potential for mitigating an overemphasis on biomedical models in such collaboration. We present a framework for how to engage in health system reform to enhance mental health services in communities that are distrustful of, or unfamiliar with biomedical approaches. Our research question was how to bridge two seemingly opposing paradigms of mental health care, and we sought to understand how the theory of transformational learning (TLT) can be applied to learning among Religious healers and biomedical practitioners in culturally appropriate ways to improve collaboration. Methods: TLT informed the development, implementation, and evaluation of an educational intervention in Addis Ababa, Ethiopia that aimed to improve delivery of mental health services at two Holy water sites. The initiative involved both psychiatrists and religious healers with extensive experience providing care to mentally ill patients. Using a focused ethnographic approach that incorporates document analysis methodology, this qualitative study examined recordings and minutes of stakeholder meetings, workshops and informal interviews with participants, analyzed for evidence of Mezirow's 11 stages of transformative learning. A participatory action approach was used to encourage practice change. Results: All participants exhibited a high degree of engagement with the of the collaborative project and described experiencing "disorienting dilemmas" by Mezirow's classic description. Opportunities to reflect separately and in large groups encouraged a re-examination of attitudes previously contributing to siloed approaches to care and led to instrumental changes in mental health care delivery and a higher degree of coordination and collaboration between psychiatrists and traditional healers. Conclusion: Our study demonstrates the utility of TLT in both the design and evaluation of initiatives aiming to bridge cross-cultural and cross-professional divides. The learning process was further enhanced by a collaborative participatory action model adjusted to accommodate Ethiopian socio-political and cultural relations.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".