Design of a Health Education Program to Manage Chronic Neck Pain: Protocol for a Development Study
Bibliographic record
Abstract
BACKGROUND: Chronic neck pain (CNP) needs attention to its physical, cognitive, and social dimensions. OBJECTIVE: We aimed to design a health education program (HEP) with a biopsychosocial approach for patients with CNP. METHODS: A literature search on CNP, health education, and biopsychosocial models was carried out. Seven physiotherapists with expertise in HEPs and chronic pain participated in three teams that evaluated the literature and prepared a synthesis document in relation to the three target topics. Experts compiled the information obtained and prepared a proposal for an HEP with a biopsychosocial approach aimed at patients with CNP. This proposal was tested in the physiotherapy units of primary care health centers belonging to the East Assistance Directorate of Madrid, and suggestions were included in the final program. RESULTS: The HEP for CNP with a biopsychosocial approach consists of 5 educational sessions lasting between 90 and 120 minutes, carried out every other day. Cognitive, emotional, and physical dimensions were addressed in all sessions, with particular attention to the psychosocial factors associated with people who have CNP. CONCLUSIONS: The proposed HEP with a biopsychosocial approach emphasizes emotional management, especially stress, without neglecting the importance of physical and recreational exercises for the individual's return to social activities. The objective of this program was to achieve a clinically relevant reduction in perceived pain intensity and functional disability as well as an improvement in quality of life in the short and medium term. TRIAL REGISTRATION: ClinicalTrials.gov NCT02703506; https://clinicaltrials.gov/study/NCT02703506. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56632.
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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 | 0.039 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.012 |
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".