TEXT4myBACK – The Development Process of a Self-Management Intervention Delivered Via Text Message for Low Back Pain
Bibliographic record
Abstract
OBJECTIVE: To develop a bank of text messages for a lifestyle-based self-management intervention for people with low back pain (LBP). DESIGN: Iterative development process. SETTING: Community and primary care. PARTICIPANTS: Fifteen researchers, clinicians, and consumer representatives participated in the concept and initial content development phase. Twelve experts (researchers and clinicians) and 12 consumers participated in the experts and consumers review phase. Full study sample of participants was N=39. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: We first conducted two 2-hour workshops to identify important domains for people with LBP, sources of content, appropriate volume, and timing of the messages. The messages were then drafted by a team of writers. Second, we invited expert researchers and clinicians to review and score the messages using a 5-item psychometric scale according to (1) the appropriateness of the content and (2) the likelihood of clinical effectiveness and to provide written feedback. Messages scoring ≤8 out of 10 points would be modified accordingly. Consumers were invited to review the messages and score them using a 5-item psychometric scale according to the utility of the content, the understanding of the content, and language acceptability and to provide feedback. Messages scoring ≤12 out of 15 points would be improved. RESULTS: Exercise, education, mood, sleep, use of care, and medication domains were identified and 82 domain-specific evidence-based messages were written. Messages received a mean score of 8.3 out of 10 points by experts. Twenty-nine messages were modified accordingly. The mean score of the messages based on consumers feedback was of 12.5 out of 15 points. Thirty-six messages were improved. CONCLUSIONS: We developed a bank of text messages for an evidence-based self-management intervention using a theory-based, iterative, codesign process with researchers, consumers, and clinicians. This article provides scientific support for future development of text message interventions within the pain field.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".