MétaCan
Menu
Back to cohort
Record W4385751656 · doi:10.1177/13591053231191919

Understanding the link between pain invalidation and depressive symptoms: The role of shame and social support in people with chronic pain

2023· article· en· W4385751656 on OpenAlexafffund
Alanna Coady, Rebecca Godard, Susan Holtzman

Bibliographic record

VenueJournal of Health Psychology · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of British Columbia
KeywordsShameMediationClinical psychologyChronic painPsychologySocial supportPopulationPsychological interventionDiscountingPain catastrophizingDepression (economics)Moderated mediationPsychiatryMedicinePsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

Pain invalidation (e.g., having pain discounted) is a risk factor for depression among people with chronic pain, but the mechanisms remain unclear. Shame is a common, yet understudied, aspect of the pain experience. This study investigated whether pain-related shame helps explain the relationship between pain discounting and heightened depressive symptoms. The secondary aim was to examine whether social support can protect against the harmful effects of discounting. Patients with chronic pain ( N = 305) were recruited from outpatient pain clinics. Participants completed an online cross-sectional survey and data were analyzed using moderated mediation analysis. Greater discounting was associated with greater depressive symptoms, and pain-related shame significantly mediated this relationship. Perceived social support attenuated the relationship between discounting and depressive symptoms. Greater attention towards pain-related shame as a treatment target is needed. Individual- and system-level interventions are required to address pain invalidation and bolster support for this population.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.050
GPT teacher head0.369
Teacher spread0.319 · 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 designObservational
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

Citations19
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Health PsychologySame topicMusculoskeletal pain and rehabilitationFrench-language works237,207