How Well Do You Expect to Recover, and What Does Recovery Mean, Anyway? Qualitative Study of Expectations After a Musculoskeletal Injury
Bibliographic record
Abstract
BACKGROUND: Expecting to recover from a musculoskeletal injury is associated with actual recovery. Expectations are potentially modifiable, although it is not well understood how injured people formulate expectations. A better understanding of how expectations are formulated may lead to better knowledge about how interventions might be implemented, what to intervene on, and when to intervene. OBJECTIVES: The objective of this study was to explore what "recovery" meant to participants, whether they expected to "recover," and how they formed these expectations. METHODS: This qualitative study used interpretive phenomenological analysis. Eighteen semistructured interviews were conducted with people seeking treatment for recent musculoskeletal injuries. RESULTS: Recovery was conceptualized as either (1) complete cessation of symptoms or pain-free return to function or (2) return to function despite residual symptoms. Expectations were driven by desire for a clear diagnosis, belief (or disbelief) in the clinician's prognosis, prior experiences, other people's experiences and attitudes, information from other sources such as the Internet, and a sense of self as resilient. CONCLUSIONS: Expectations appear to be embedded in both hopes and fears, suggesting that clinicians should address both when negotiating realistic goals and educating patients. This approach is particularly relevant for cases of nonspecific musculoskeletal pain, where diagnoses are unclear and treatment may not completely alleviate pain.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".