Self‐management support for chronic pain in primary care: a cross‐sectional study of patient experiences and nursing roles
Bibliographic record
Abstract
AIMS: The aim of this study was to describe chronic pain self-management from the perspective of individuals living with chronic pain in the context of primary care nursing. BACKGROUND: Self-management is a key chronic pain treatment modality and support for self-managing chronic pain is mainly provided in the context of primary care. Although nurses are optimally suited to facilitate self-management in primary care, there is a need to explore opportunities for optimizing their roles. DESIGN: Two cross-sectional studies. METHODS: The Chronic Pain Self-Management Survey was conducted in 2011-2012 to explore the epidemiology and self-management of chronic pain in Canadian adults. The questionnaire was distributed to 1504 individuals in Ontario. In 2011, the Primary Care Nursing Roles Survey was distributed to 1911 primary care nurses in Ontario to explore their roles and to determine the extent to which chronic disease management strategies, including support for self-management, were implemented in primary care. RESULTS: Few respondents to the pain survey identified nurses as being the 'most helpful' facilitator of self-management while physicians were most commonly cited. Seventy-six per cent of respondents used medication to manage their chronic pain. Few respondents to the nursing survey worked in practices with specific programmes for individuals with chronic pain. Individuals with chronic pain identified barriers and facilitators to self-managing their pain and nurses identified barriers and facilitators to optimizing their role in primary care. CONCLUSION: There are several opportunities for primary care practices to facilitate self-management of chronic pain, including the optimization of the primary care nursing role.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".