What Does ‘Recovery’ Mean to People with Neck Pain? Results of a Descriptive Thematic Analysis
Bibliographic record
Abstract
OBJECTIVES: To describe the meaning of being recovered as perceived by people with chronic mechanical neck pain. METHODS: To determine the way people with neck pain would describe a recovered state a descriptive thematic approach was used. A nominal focus group technique, written reflections, and one-on-one semi-structured interviews were used to collect sufficient data. Data from the focus groups were analyzed both through vote tallying and thematic analysis. Reflections and interviews were analyzed thematically by two independent researchers. Triangulation and member-checking were employed to establish trustworthiness of results. RESULTS: A total of 35 people, primarily females with neck pain of traumatic origin, participated in this study. Thematic analysis identified 6 themes that adequately described the data: absent or manageable symptoms, having the physical capacity one ought to have, participation in life roles, feeling positive emotions, autonomy & spontaneity, and re-establishing a sense of self. Member checking and triangulation suggested data saturation and accuracy of the generated themes. DISCUSSION: Recovery from neck pain appears to be informed by factors that fit with existing models of health, quality of life and satisfaction. Basing recovery solely on symptom or activity-level measures risks inaccurate estimates of recovery trajectories from traumatic or non-traumatic neck 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 | 0.049 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".