Developing the Bridges self-management programme for New Zealand stroke survivors: A case study
Bibliographic record
Abstract
Background/aim This case study describes the adaptation of the UK-developed Bridges stroke self-management programme (Bridges SMP) into a version relevant and acceptable to the New Zealand (NZ) context. Methods Stakeholder consultation and qualitative methodology explored the acceptability and relevance of the Bridges SMP in NZ. Focus group discussions were held with stroke survivors (n=60) and neurorehabilitation therapists (n= 17). Semi-structured interviews were conducted with 22 stroke survivors. Based on data gathered the authors culturally and contextually adapted the accompanying Bridges SMP workbook. This study piloted the adapted programme with six stroke survivors and used semi-structured interviews to explore their perceptions of it. Findings The Bridges SMP was considered acceptable and beneficial for developing skills to self-manage recovery following stroke. The main recommended adaptation was the inclusion of NZ stories into the accompanying workbook. Four themes reflected the six pilot study participants' perceptions of the programme: you are not alone, reflection and taking action, life continues after stroke, and taking responsibility. Conclusions The Bridges SMP was considered relevant and only required moderate adaptation for use in NZ. The process the authors undertook to contextualise Bridges SMP for NZ will provide guidance to the programme's introduction into other international regions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".