Delivery of pain coping skills training program to manage chronic pain related to osteoarthritis: the primary care provider role
Bibliographic record
Abstract
Osteoarthritis (OA) is the primary form of arthritis that affects a large portion of the Canadian population. With expected increases in OA prevalence over time, the magnitude of outcomes related to inadequate pain control will place further burdens on society and healthcare resources. Pain Coping Skills Training (PCST) is an intervention protocol derived from cognitive behavioural therapy and has traditionally been delivered by clinical psychologists to manage chronic pain related to OA. However, few providers in the primary care setting are trained in PCST. The lack of trained primary care providers creates a barrier to patient access in the community setting, which should be addressed. An integrative literature review has been conducted to identify if primary care providers, who work in primary care settings, can deliver PCST interventions to decrease pain interference and improve quality of life outcomes in adult patients diagnosed with OA. The results are discussed within the context of Canada's primary care practice. Eleven articles were reviewed using Whittemore and Knafl’s approach to the integrative literature review. The results suggest that PCST interventions are both practical and possible among providers that do not possess a background in mental health specialization. Thus, primary care providers are encouraged to obtain educational competency to deliver this effective therapy to manage the adverse psychological effects on chronic pain related to OA. This way, providers can offer a biopsychosocial approach in managing OA while also playing an essential role in improving access to PCST interventions in the primary care setting. Recommendations for facilitating the uptake of PCST interventions are discussed, and specific strategies for its use in primary care are presented.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".