Co-Developing a Radical Mental Health Doula Model of Support
Bibliographic record
Abstract
Feminist Participatory Action Research (FPAR) has the potential to create impactful research outcomes, challenge institutional hierarchies and disrupt conditions that oppress and marginalize women in the research process. This paper reflects on the application of FPAR as a methodology during the development of an innovative Radical Mental Health Doula (RMHD) framework and the accompanying training curriculum. Women and their experiences with mental health systems and services are at the centre of this project. Experts through their own experience, women co-researchers (WCRs) were instrumental in identifying problems and determining how to address gaps in what they recognized as an often cruel, fragmented and dehumanizing model of mental health care. The FPAR approach allowed us to question the roles of expert, researcher and subject. This enabled an exploration of how women’s voices and experience, which are traditionally silenced, can challenge hierarchical and patriarchal practices in mental health systems and research. Reflecting on the use of FPAR, through an analysis of data from consultation meetings with WCRs, we identified three key practices that led to the successful application of this methodology in the RMHD project. In this paper we highlight the voices of women co-researchers to examine 1. Relationship building, 2. Inquiry with women co-researchers and respect for lived experience, and 3. Holding space to share vulnerability and emotion in the FPAR process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".