Advocacy training for young family doctors in primary mental health care: a report and global call to action
Bibliographic record
Abstract
Background The World Health Organization (WHO) recognises the essential role of mental health in achieving health for all; its mental health action plan calls for more effective leadership for mental health and the provision of community-based, integrated care. 1 However, integrating mental health care into primary care is a challenging, transformational change that requires more than clinical knowledge. 2 It depends on strong advocacy, leadership, and change management: skills that can be learnt. 3,4 Project The Farley Health Policy Centre (FHPC) partnered with the World Organization of Family Doctors (WONCA) to develop and pilot a global curriculum to enable learners to lead practice transformation and be empowered with policy-influencing skills to advocate for their patients, to promote and enhance primary care mental health. We recruited 12 young family doctors, of whom seven were women and ten were from low- and middle-income countries (LMICs), as shown in Figure 1. The programme began with a survey of learners' needs and aspirations, and an expectation that each would self-identify a practice transformation goal. Faculty and learners took part in a two-phase learning evaluation. Funding from WONCA was provided for logistics and evaluation. A small stipend was offered to each learner on successful course completion. Faculty gave their time pro bono. The programme was conducted between March and October 2020. The evaluation process was approved by the University of Liverpool Health and Life Sciences Research Ethics Committee. The learners were divided into two learning cohorts. Sessions were facilitated by two leaders and supported by four mentors. The educational content was delivered twice (to accommodate differing time zones) in six 90-minute monthly virtual sessions. The topics were: • Introduction to mental health integration
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".