Using intervention mapping to develop ‘ROSE’: an intervention to support peer workers in overdose response settings
Bibliographic record
Abstract
BACKGROUND: Peer workers (those with lived/living experience of substance use working in overdose response settings) are at the forefront of overdose response initiatives in British Columbia (BC). Working in these settings can be stressful, with lasting social, mental and emotional impacts. Peer workers have also been disproportionately burdened by the current dual public health crises characterized by the onset of the COVID-19 pandemic and rise in illicit drug overdose deaths. It is therefore critical to develop supports tailored specifically to their realities. METHODS: We used the six steps outlined in the Intervention Mapping (IM) framework to identify needs of peer workers and design an intervention model to support peer workers in overdose response settings. RESULTS: Eight peer-led focus groups were conducted in community settings to identify peer workers' needs and transcripts were analyzed using interpretive description. The strategies within the intervention model were informed by organizational development theory as well as by lived/living experience of peer workers. The support needs identified by peer workers were categorized into three key themes and these formed the basis of an intervention model titled 'ROSE'; R stands for Recognition of peer work, O for Organizational support, S for Skill development and E for Everyone. The ROSE model aims to facilitate cultural changes within organizations, leading towards more equitable and just workplaces for peer workers. This, in turn, has the potential for positive socio-ecological impact. CONCLUSIONS: Centering lived/living experience in the intervention mapping process led us to develop a framework for supporting peer workers in BC. The ROSE model can be used as a baseline for other organizations employing peer workers.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".