A mixed methods study to investigate needs assessment for knee pain and disability: population and individual perspectives
Bibliographic record
Abstract
BACKGROUND: The new Musculoskeletal Services Framework outlines the importance of health care needs assessment. Our aim was to provide a model for this for knee pain and disability, describing felt need (individual assessment of a need for health care) and expressed need (demand for health care). This intelligence is required by health care planners in order to implement the new Framework. METHODS: A multi-method approach was used. A population survey (n = 5784) was administered to adults aged 50+ registered with 3 general practices. The questionnaire contained a Knee Pain Screening Tool to identify the prevalence of knee pain and health care use in the population, and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Survey responders who scored "severe" or "extreme" on at least one item on the pain or physical function scale on the WOMAC were categorised into "severe" groups. Qualitative interviews were undertaken with 22 survey responders to explore in detail the experience of living with knee pain and disability. A sample of interviewees (n = 10) completed an open format patient diary to explore the experience of knee pain in everyday life. RESULTS: The 12-month period prevalence of knee pain was 49.5%, of which half was severe. Severe difficulties were reported with domestic duties, bending, bathing, climbing stairs and getting in or out of a car. Some self-care is occurring. The majority (53%) of responders with severe pain or disability had not consulted their GP in the last 12 months. The qualitative study revealed reasons for this including a perception that knee pain is part of normal ageing, little effective prevention and treatment is available and the use of medications causes side effects and dependency. CONCLUSION: This study adds to previous work by highlighting a gap between felt and expressed need and the reasons for this mismatch. There is evidence of self-management, but also missed opportunities for effective interventions (e.g. lifestyle advice). A targeted and integrated approach between clinicians and health care planners for primary and secondary prevention is required if aspects of the new Musculoskeletal Services Framework are to be successfully implemented.
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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 | 0.031 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".