Health care providers’ understanding of self-management support for people with chronic low back pain in Ethiopia: an interpretive description
Bibliographic record
Abstract
BACKGROUND: Healthcare providers play a key role in supporting people with chronic low back pain to self-manage their condition. The study aimed at exploring how health care providers understand and conceptualize self-management and how they provide self-management support for people with chronic low back pain in Ethiopia. METHODS: Health care providers who have supported people with low back pain, including medical doctors and physiotherapists, were approached and recruited from three hospitals in Ethiopia. This study employed an interpretive descriptive approach using semi-structured interviews. FINDINGS: Twenty-four participants (7 women; 17 men) with a median age of 28 (range 24 to 42) years and a median of 9.5 years (range 1 to 11 years) of helping people with chronic low back pain were interviewed. Seven major themes related to health care providers' understanding of self-management support for people with chronic low back pain in Ethiopia emerged. The findings show that self-management was a new concept to many and health care providers' had a fragmented understanding of self-management. They used or suggested several self-management support strategies to help people with CLBP self-manage their condition without necessarily focusing on enhancing their self-efficacy skills. The participants also discussed several challenges to facilitate self-management support for people with chronic low back pain. Despite the lack of training on the concept, the providers discussed the potential of providing self-management support for people with the condition. CONCLUSIONS: Self-management was a new concept to health care providers. The providers lack the competencies to provide self-management support for people with chronic low back pain. There is a need to enhance the health care providers' self-management support competencies through training.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".