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Record W3196854819 · doi:10.3138/jmvfh-2021-0054

Chronic pain program management outcomes: Long-term follow-up for Veterans and civilians

2021· article· en· W3196854819 on OpenAlexaffvenue
Eleni G. Hapidou, Eric Pham, Kate Bartley, Jennifer Anthonypillai, Sonya Altena, Lisa Patterson, Ramesh Zacharias

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2021
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster Children's HospitalNiagara Health SystemMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsMedicineDepression (economics)AnxietyRehabilitationChronic painPhysical therapyPain managementPsychiatryTerm (time)Clinical psychology

Abstract

fetched live from OpenAlex

LAY SUMMARY Interdisciplinary pain rehabilitation programs are effective in treating chronic pain. Not many studies have explored how Veterans differ from civilians in responding to treatment. In this study, several measures were administered at different time points to examine and compare the long-term treatment outcomes of Veteran and civilian men and women. Results from 67 participants showed an overall long-term improvement in levels of pain-related disability, anxiety, and depression, as well as many other pain-related variables. While no differences in treatment outcomes between Veterans and civilians were found, men and women showed some differences. Women reported higher depressive symptoms overall and more pain-related disability than men at follow-up from the program. This study demonstrates the long-term effectiveness of interdisciplinary pain management programs regardless of Veteran status. It highlights some differences between genders. Previous studies have not compared the long-term outcomes of Veterans and civilians from an interdisciplinary program.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.336
Teacher spread0.311 · 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 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

Citations3
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Military Veteran and Family Health→Same topicMusculoskeletal pain and rehabilitation→French-language works237,207→