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Record W4243839017 · doi:10.21203/rs.3.rs-120637/v1

Towards Health Equity for People Experiencing Chronic Pain and Social Marginalization

2020· preprint· en· W4243839017 on OpenAlexafffund
Bruce Wallace, Colleen Varcoe, Cindy Holmes, Mehmoona Moosa-Mitha, Gregg Moor, Maria Hudspith, Kenneth D. Craig

Bibliographic record

VenueResearch Square · 2020
Typepreprint
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaMichael Smith Health Research BC
KeywordsEquity (law)Health equityChronic painSocial equalityPsychologyPolitical sciencePsychiatryEconomicsHealth careEconomic growthMarket economy

Abstract

fetched live from OpenAlex

Abstract Objective: For people who experience inequities and structural violence, pain and related care are inexorably linked to experiences of injustice and stigma.Methods: A community-based qualitative study included four focus groups with 36 people living with pain from groups known to experience high levels of inequities and structural violence including an Indigenous group, a LGBTQ2S group, and two newcomer and refugee groups. Results: Pain was entangled with and shaped by: social locations and identities, experiences of violence, trauma and related mental health issues, experiences of discrimination, stigma and dismissal, experiences of inadequate and ineffective health care, and the impacts of these intersecting experiences.Conclusions: Equity-oriented responses to chronic pain would establish pain not only as a biomedical issue but as a social justice issue. The EQUIP Framework is an approach to integrating trauma- and violence-informed care; culturally-safe care; and harm reduction in health care that may hold promise for being tailored to people experiencing pain and social marginalization.

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)0.015
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.020
Scholarly communication0.0060.006
Open science0.0010.019
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.000

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 head0.111
GPT teacher head0.480
Teacher spread0.370 · 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 designTheoretical or conceptual
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

Citations1
Published2020
Admission routes2
Has abstractyes

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