Gestion de la douleur chronique par les infirmières des Groupes de médecine de famille
Bibliographic record
Abstract
INTRODUCTION: Thousands of people treated in primary care are currently experiencing chronic pain (CP), for which management is often inadequate. In Quebec, nurses in family medicine groups (FMGs) play a key role in the management of chronic health problems. OBJECTIVE: The present study aimed to describe the activities performed by FMG nurses in relation to CP management and to describe barriers to those activities. METHOD: A descriptive correlational cross-sectional postal survey was used. The accessible population includes FMG nurses on the Ordre des infirmières et infirmiers du Québec list. All nurses on the list who provided consent to be contacted at home for research purposes were contacted. A self-administered postal questionnaire (Pain Management Activities Questionnaire) was completed by 53 FMG nurses. RESULTS: Three activities most often performed by nurses were to establish a therapeutic relationship with the client; discuss the effectiveness of therapeutic measures with the physician; and conduct personalized teaching for the patient. The average number of individuals seen by interviewed nurses that they believe suffer from CP was 2.68 per week. The lack of knowledge of possible interventions in pain management (71.7%) and the nonavailability of information on pain management (52.8%) are the main barriers perceived by FMG nurses. CONCLUSION: FMG nurses are currently performing few activities in CP management. The nonrecognition of CP may explain this situation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".