Patients’ and staffs’ experiences of clinical support after a lower limb reconstruction: An interview study
Bibliographic record
Abstract
Objective: To explore patient and healthcare professional perspectives on post-lower limb reconstruction support. Design: A qualitative study . Setting: One-to-one semi-structured interviews undertaken via video conferencing software or over the telephone. Participants: Thirty-two patients (who had undergone reconstructive surgery due to trauma, malunion, non-union, infection or congenital issues treated by internal or external fixation) and 22 orthopaedic healthcare professionals (surgeons, nurses and physiotherapists) were interviewed between November 2020 and June 2021. Results: Thematic analysis was undertaken on data surrounding clinical support. Four themes were generated: being involved and feeling informed, physiotherapy as a motivator for recovery, the importance of timely access to physiotherapy, and mental health support. Patients valued being involved in decisions about their care and feeling informed. Supportive nurses played a key role, with patients appreciating clear communication and empathy. Trust in the clinical team eased anxiety, whilst a lack of information caused stress and dissatisfaction. Post-surgery, information on recovery, access to physiotherapy, and hospital contacts were crucial. Physiotherapy was vital for regaining mobility, providing motivation, and promoting confidence. Access to mental health support was limited, though patients appreciated emotional support from physiotherapists and hospital staff. Conclusion: By providing comprehensive support, healthcare professionals can optimise patient satisfaction with care, enhance psychological well-being, and facilitate the successful rehabilitation and reintegration of patients back into their daily lives.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".