Are Patient and Carer Experiences Mirrored in the Practice Reviews of Self-management Support (Prisms) Provider Taxonomy?
Bibliographic record
Abstract
INTRODUCTION: Patient self-management support is central to care for long term conditions and for integrated care. Patients and their carers are the final arbiter of whether support for self-management has been effective. A new taxonomy lists 14 categories of provider activities that support patient self-management (Practical Reviews in Self-Management Support, PRISMS). We asked whether we could recognise these provider activities in narratives from patients and carers. We sought to extend the theoretical framework of the taxonomy to include the view from patient and carers. METHODS: We interviewed 28 patients and family carers in a case study of primary health care in New Zealand in 2015 to determine which components of the taxonomy were visible. We drew on interviews with clinicians and organisation persons to explain case study context. RESULTS: We found, within patient and carer data, evidence of all 14 components of provider self-management support. The overarching dimensions of the taxonomy helped reveal an intensity and consistency of provider behaviour that was not apparent considering the individual components. CONCLUSIONS: Patient and carer data mapped to provider activities. The taxonomy was not explicit on provider relationships and engagement with, or separate support needs of, patients and carers.
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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 | 0.031 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".