Patient satisfaction with advanced physiotherapy practice internationally: Protocol for a systematic mixed studies review
Bibliographic record
Abstract
RATIONALE: Patient satisfaction is a complex construct consisting of human and system attributes. Patient satisfaction can afford insight into patient experience, itself a key component of evaluating healthcare quality. Internationally, advanced physiotherapy practice (APP) extends across clinical fields and is characterised as a higher level of practice with a high degree of autonomy and complex decision making. Patient satisfaction with APP appears positive. While evidence synthesis of patient satisfaction with APP exists, no systematic review has synthesised evidence across clinical fields. Therefore, the objectives of this systematic review are 1) to evaluate patient satisfaction with APP internationally, and 2) to evaluate human and system attributes of patient satisfaction with APP. MATERIALS AND METHODS: A systematic mixed studies review using a parallel-results convergent synthesis design will be conducted. Searches of Medline, Embase, Web of Science, CINAHL, Cochrane, PEDro and grey literature databases will be conducted from inception to 18/7/2023. Studies of APP (World Physiotherapy definition) whereby practitioners a) have advanced clinical and analytical skills that influence service improvement and provide clinical leadership, b) have post-registration masters level specialisation (or equivalence), c) deliver safe, competent care to patients with complex needs and d) may use particular occupational titles; that measure patient satisfaction across all clinical fields and countries will be included. Two reviewers will screen studies, extract data, assess methodological quality of included studies (mixed methods appraisal tool), and contribute to data synthesis. Quantitative data will undergo narrative synthesis (textual descriptions) and qualitative data thematic synthesis (analytical themes). Integration of data syntheses will inform discussion. IMPLICATIONS: This systematic review will provide insight into patient satisfaction with APP internationally, exploring attributes that influence satisfaction. This will aid design, implementation, or improvement of APP and facilitate the delivery of patient-centred, high-quality healthcare. Lastly, this review will inform future methodologically robust research investigating APP patient satisfaction and experience.
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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 | 0.104 | 0.087 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.017 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.088 | 0.014 |
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".