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Record W4399497065 · doi:10.1080/24740527.2024.2358332

Advancing chronic pain care in Canada: History and impact of the Canadian Pain Task Force

2024· review· en· W4399497065 on OpenAlexaffabout
Fiona Campbell, Manon Choinière, Hani El‐Gabalawy, Jacques Laliberté, Michael Sangster, Jaris Swidrovich, Linda Wilhelm, Maria Hudspith

Bibliographic record

VenueCanadian Journal of Pain · 2024
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsSpinal Cord Injury BCIzaak Walton Killam Health CentreUniversity of ManitobaUniversité de MontréalCanadian Arthritis Patient AllianceCentre Hospitalier de l’Université de MontréalCanadian Physiotherapy AssociationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMandateTask forceTask (project management)NarrativeChronic painRepresentation (politics)Work (physics)PsychologyPublic relationsMedicineNursingPolitical sciencePublic administrationPhysical therapyManagementEngineeringLaw

Abstract

fetched live from OpenAlex

In 2019, Health Canada established the Canadian Pain Task Force. Through this commitment, Canada joined other countries, such as the United States and Australia, in creating a national-level mechanism to support work in the area of chronic pain. This article provides a historical narrative of national and regional advocacy and efforts that led to creation of the Task Force, the broad representation of its members as well as its mandate and goals. Subsequently it outlines the Task Force’s progression through three distinct phases, each marked by extensive consultation and culminating in a comprehensive report submitted to Health Canada. A particular focus is placed on the third phase, which resulted in the formulation of the Action Plan for Pain in Canada, and we present an overview of the recommendations contained therein. Moreover, the article situates the Canadian Pain Task Force within the broader movement to transform how pain is recognized, understood, and treated in Canada. It highlights initial steps taken to address identified priorities, indicating a proactive approach towards effecting meaningful change.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.901
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.267
Teacher spread0.257 · 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 teacher head, not a consensus.

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

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

Citations8
Published2024
Admission routes2
Has abstractyes

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