Determinants of patient satisfaction in outpatient musculoskeletal physiotherapy: a systematic, qualitative meta-summary, and meta-synthesis
Bibliographic record
Abstract
Purpose: To identify and synthesise patient-identified factors that influence satisfaction with outpatient musculoskeletal physiotherapy (O-MSK).Methods: A systematic, qualitative meta-summary and meta-synthesis was conducted by accessing six electronic databases: CINAHL, Embase, MEDLINE, Scopus, Web of Science, and Wiley Online Library, from inception to March 2017. Additional studies were identified by using a “berry-picking” method. Search limits were: primary studies; English language; and involving human subjects. Qualitative peer-reviewed articles describing patient satisfaction in O-MSK were eligible for inclusion. Two reviewers critically appraised eligible studies independently using the critical appraisal of skills programme tool for qualitative studies. Extracted verbatim data of included studies were synthesised using the meta-summary and meta-synthesis by using a purpose-designed form.Results: Eleven studies were included in the article. Factors influencing patient satisfaction were grouped into six broad themes: 1) clinical outcomes; 2) physiotherapist features; 3) patient features; 4) physiotherapist-patient relationship; 5) treatment features, and 6) healthcare setting features.Conclusions: These findings suggest that patient satisfaction in O-MSK is a multidimensional construct influenced by individual patient/provider, clinical, and contextual factors. Future reviews should include a synthesis of findings from both qualitative and quantitative studies to establish a fully comprehensive understanding of this complex health phenomenon.Implications for rehabilitationPatient satisfaction in outpatient musculoskeletal physiotherapy is affected by different factors, thus reflecting a multidimensional construct;Single determinants are not sufficient to affect patient satisfaction;Patient satisfaction is influenced individual patient/provider, clinical outcomes, and contextual factors;Further studies should be designed to investigate the relationships among these factors.
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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 | 0.076 | 0.188 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".