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Record W4393279278 · doi:10.1080/24740527.2024.2335500

Attending to Marginalization in The Chronic Pain Literature: A Scoping Review

2024· review· en· W4393279278 on OpenAlexafffund
Laura Connoy, Michelle Solomon, Riana Longo, Abhimanyu Sud, Joel Katz, Craig Dale, Meagan Stanley, Fiona Webster

Bibliographic record

VenueCanadian Journal of Pain · 2024
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsHealth Sciences CentreYork UniversityUniversity of TorontoSunnybrook Health Science CentreWestern University
FundersCanadian Institutes of Health Research
KeywordsChronic painMedicinePsychologyPhysical therapyPsychotherapist

Abstract

fetched live from OpenAlex

Background: There has been a recent and, for many within the chronic pain space, long-overdue increase in literature that focuses on equity, diversity, inclusion, and decolonization (EDI-D) to understand chronic pain among people who are historically and structurally marginalized. Aims: In light of this growing attention in chronic pain research, we undertook a scoping review of studies that focus on people living with chronic pain and marginalization to map how these studies were carried out, how marginalization was conceptualized and operationalized by researchers, and identify suggestions for moving forward with marginalization and EDI-D in mind to better support people living with chronic pain. Methods: We conducted this scoping review using critical analysis in a manner that aligns with dominant scoping review frameworks and recent developments made to scoping review methodology as well as reporting guidelines. Results: Drawing on 67 studies, we begin with a descriptive review of the literature followed by a critical review that aims to identify fissures within the field through the following themes: (1) varying considerations of sociopolitical and socioeconomic contexts, (2) conceptual conflations between sex and gender, and (3) differing approaches to how people living with chronic pain and marginalization are described. Conclusion: By identifying strengths and limitations in the research literature, we aim to highlight opportunities for researchers to contribute to a more comprehensive understanding of marginalization in chronic pain experiences.

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)0.015
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.511
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
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 head0.032
GPT teacher head0.364
Teacher spread0.333 · 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 designSystematic review
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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