Experiences, views and perceptions of recovery following musculoskeletal trauma of patients and physiotherapists: a qualitative study
Bibliographic record
Abstract
The aim of this qualitative phenomenology study using two methods (semi-structured interviews, focus groups), was to explore patients' and physiotherapists' views and perceptions of recovery, and what constitutes successful recovery following musculoskeletal trauma within the early stages of recovery. Participants were recruited from one major trauma centre in the United Kingdom and data collected via Microsoft Teams, or via a telephone call. Inclusion criteria for patient interviews: purposive sample of adults (≥18 years) who sustained a traumatic musculoskeletal injury, admitted as an inpatient within 4 weeks of injury, mental capacity, and able to communicate in English. Purposive sampling included age, gender, and injury characteristics. Focus group inclusion criteria: physiotherapists with experience managing patients with musculoskeletal trauma. Interviews and focus groups were informed by an evidenced based topic guide, audio recorded and transcribed verbatim. Trustworthiness of the data was strengthened using multiple strategies, e.g., member checking. Interpretative Phenomenological Analysis was used for the patient interviews and the Kreuger Framework for the focus groups. Participants included 17 patient interviews and 10 physiotherapists in two focus groups. Three themes emerged from patient interviews: understanding and impact of the accident and injuries, the early stages of recovery and physiotherapy, and healthcare and setting influences. Eight themes emerged from the focus groups: process of recovery, what is being fully recovered, it's more than just communicating with the patient, psychological impact of trauma affecting recovery, system influences/resources for recovery, influencers to recovery, barriers to using patient reported outcome measures to evaluate recovery, and what actually is useful to measure in trauma? Recovery following musculoskeletal trauma is complex, individual and focused on returning to 'normal'. Similarities across patient and physiotherapist views of recovery exist. Differences between participant groups were evident, centred on communication and what is important to the patient in their recovery.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".