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Record W4250583002 · doi:10.24124/2019/58985

The use of brief cognitive behavioral therapy interventions in the treatment of chronic non-cancer pain in the primary care setting

2019· dissertation· en· W4250583002 on OpenAlexaffabout
Megan Ann Newton

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsPsychosocialReferralCognitive behavioral therapyPsychological interventionInterimMedicineCognitionPrimary careHealth careChronic painCoping (psychology)Cognitive therapyPsychologyPsychotherapistPsychiatryNursingFamily medicine

Abstract

fetched live from OpenAlex

As one of the foremost causes of healthcare resource consumption and disability among Canadian adults, chronic non-cancer pain (CNCP) requires significant attention within healthcare delivery and research. While CNCP treatment is typically guided by pharmacotherapeutics, current literature illustrates that Cognitive Behavioral Therapy (CBT) as a CNCP treatment can promote effective pain coping strategies, thereby improving pain and psychosocial outcomes. Cognitive Behavioral Therapy services are constrained, in particular due to limited access and referral to mental health professionals who provide these services. To improve access to CBT services and close gaps in CNCP care, primary care providers could offer brief CBT in their practices. Brief CBT (bCBT) delivered in primary care settings would provide active treatment for CNCP as well as interim treatment for patients awaiting referral to full-service CBT, should that service be required.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.042
GPT teacher head0.375
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

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