Applying Normalisation Process Theory to a peer-delivered complex health intervention for people experiencing homelessness and problem substance use
Bibliographic record
Abstract
BACKGROUND: The Supporting Harm Reduction through Peer Support (SHARPS) study involved designing and implementing a peer-delivered, harm reduction intervention for people experiencing homelessness and problem substance use. Normalisation Process Theory (NPT) provided a framework for the study. METHODS: Four Peer Navigators (individuals with personal experience of problem substance use and/or homelessness) were recruited and hosted in six third sector (not-for-profit) homelessness services in Scotland and England (United Kingdom). Each worked with participants to provide practical and emotional support, with the aim of reducing harms, and improving well-being, social functioning and quality of life. NPT guided the development of the intervention and, the process evaluation, which assessed the acceptability and feasibility of the intervention for this cohort who experience distinct, and often unmet, health challenges. While mixed-methods data collection was undertaken, this paper draws only on the qualitative data. RESULTS: The study found that, overall, the intervention is feasible, and acceptable to, the intervention participants, the Peer Navigators and staff in host settings. Some challenges were encountered but these were outweighed by benefits. NPT is particularly useful in encouraging our team to focus on the relationship between different aspects of the intervention and context(s) and identify ways of maximising 'fit'. CONCLUSIONS: To our knowledge, this is the first application of NPT to this cohort, and specifically by non-clinicians (peers) in non-healthcare settings (homelessness services). Our application of NPT helped us to identify ways in which the intervention could be enhanced, with the key aim of improving the health/well-being of this underserved group.
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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 | 0.033 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".