Faculty-Student-Service User Collaboration: Community-based Action Research Regarding Service User Involvement in Mental Health and Addiction Policy
Bibliographic record
Abstract
Growing out of a 2015 Social Work course at Memorial University (Newfoundland), this panel explores the issue of Faculty–Student–Service User Collaboration in Community-based scholarship regarding population groups typically excluded from research and policy development. This course produced two successful forms of collaboration between the professor, his students, and several community groups. First, in order to facilitate direct drug/service user engagement in research and policy-making, Dr. Smith—whose research emphasizes engaging people with lived experience in all aspects of mental health and addiction policy and practice—founded a local organization for people who use drugs. Detailing the establishment of the Drug User Group, the panel interrogates both the role that Dr. Smith’s students played in promoting the group, and how this project effectively served to inspire several students’ final projects. Encouraging his students to create activist-oriented ‘zines’/booklets as opposed to traditional essays, in the second case, several of Dr. Smith’s students actively collaborated with local grassroots organizations, producing several exceptional ‘zines,’ encompassing the issues of (1) psychiatric survivor rights, (2) the importance of directly engaging mental health and addiction service users, and (3) the legal rights of sex workers. Consisting of a reflection on the impact of politically engaged action research regarding ‘user involvement’, this panel consists of an introduction by Dr. Smith detailing the collaborative establishment of the St. John’s Drug User Group, followed by related commentary from four of his students detailing their respective policy ‘zines,’ each of which was composed in direct consultation with service users.
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 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 | 0.064 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.019 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".