“Come and share your story and make everyone cry”: complicating service user educator storytelling in mental health professional education
Bibliographic record
Abstract
It has become relatively common practice within health professional education to invite people who have used mental health and social care services (or service user educators) to share their stories with health professional learners and students. This paper reports on findings from a postcritical ethnographic study of the practice of service user involvement (SUI), in which we reflexively inquired into conceptualizations of service user educators' knowledge contributions to health professional education in the accounts of both service user- and health professional educators. This research was conducted in response to recent calls for greater scrutiny surrounding the risks, challenges, and complexities inherent in involving service users in health professional education spaces. 'Story/telling' was identified as a pronounced overarching construct in our analysis, which focuses on participants' reports of both the obvious and more subtle tensions and complexities they experience in relation to storytelling as a predominant tool or approach to SUI. Our findings are presented as three distinct, yet overlapping, themes related to these complexities or tensions: (a) performative expectations; (b) the invisible work of storytelling; and (c) broadening conceptualizations of service user educators' knowledge. Our findings and discussion contribute to a growing body of literature which problematizes the uncritical solicitation of service user educators' stories in health professional education and highlights the need for greater consideration of the emotional and epistemic labour expected of those who are invited to share their stories. This paper concludes with generative recommendations and reflexive prompts for health professional educators seeking to engage service user educators in health professional education through the practice of storytelling.
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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 | 0.045 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.033 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".