Assessing the organizational impact of patient involvement: a first STEPP
Bibliographic record
Abstract
Purpose - Patient involvement in the design and improvement of health services is increasingly recognized as an essential part of patient-centred care. Yet little research, and no measurement tool, has addressed the organizational impacts of such involvement. The paper aims to discuss these issues. Design/methodology/approach - The authors developed and piloted the scoresheet for tangible effects of patient participation (STEPP) to measure the instrumental use of patient input. Its items assess the magnitude of each recommendation or issue brought forward by patients, the extent of the organization's response, and the apparent degree of patient influence on this response. In collaboration with teams (staff) from five involvement initiatives, the authors collected interview and documentary data and scored the STEPP, first independently then jointly. Feedback meetings and a "challenges log" supported ongoing improvement. Findings - Although researchers' and teams' initial scores often diverged, the authors quickly reached consensus as new information was shared. Composite scores appeared to credibly reflect the degree of organizational impact, and were associated with salient features of the involvement initiatives. Teams described the STEPP as easy to use and useful for monitoring and accountability purposes. The tool seemed most suitable for initiatives in which patients generated novel, concrete recommendations; less so for broad public consultations of which instrumental use was not a primary goal. Originality/value - The STEPP is a promising, first-in-class tool with potential usefulness to both researchers and practitioners. With further research to better establish its reliability and validity, it could make a valuable contribution to full mixed-methods evaluation of patient involvement.
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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 | 0.078 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".