Is knowledge a barrier to implementing low back pain guidelines? Assessing the knowledge of Israeli family doctors
Bibliographic record
Abstract
OBJECTIVES: To measure knowledge of Israeli low back pain (LBP) clinical practice guidelines among different subgroups of primary care doctors, prior to designing an intervention programme to enhance guideline adherence in practice. STUDY DESIGN: Confidential mailed survey questionnaire. SETTING: Family practices in the Haifa and western Galilee district, Israel. PARTICIPANTS: Random sample of 163 primary care doctors. A total of 134 doctors (82%) completed the questionnaire. MAIN OUTCOME MEASURES: A Multiple Choice Questionnaire measuring knowledge of the LBP guidelines. Instrument reliability and inter-item reliability were tested in a pilot phase. Content validity was assured by having the Israeli LBP guideline authors involved in a consensus procedure. RESULTS: Distribution of test scores significantly differentiated professional levels and background variables, demonstrating the instrument reliability. Cronbach's alpha was above 0.91. The average test score was 67.7 [standard deviation (SD) 16.2], family doctors had average scores of 75.2 (SD 9.8), general practitioners (GPs) 57.9 (SD 19) and family practice residents 67.4 (SD 13.2). The difference between the test average scores of family doctors, GPs and residents was significant (P < 0.001). Significant differences were also found for specific variables including the doctor's age, country of medical training and self-report familiarity with the LBP guidelines. CONCLUSIONS: Striking differences exist between subgroups of primary care doctors regarding their knowledge of LBP guidelines. These differences will require the design of multiple interventions tailored to each subgroup.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".