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Record W2101046768 · doi:10.1186/1472-6874-4-s1-s17

Chronic Pain: The Extra Burden on Canadian Women

2004· article· en· W2101046768 on OpenAlexaffabout
Marta Meana, Robert Cho, Marie DesMeules

Bibliographic record

VenueBMC Women s Health · 2004
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsHealth Canada
Fundersnot available
KeywordsChronic painMedicineEthnic groupDepression (economics)Health carePopulationDemographyGerontologyPhysical therapyEnvironmental health

Abstract

fetched live from OpenAlex

HEALTH ISSUE: Chronic pain is a major health problem associated with significant costs to both afflicted individuals and society as a whole. These costs seem to be disproportionately borne by women, who generally have higher prevalence rates for chronic pain than do men. KEY FINDINGS: Data obtained from 125,574 respondents to the Canadian Community Health Survey (2000-2001) indicated that 18% of Canadian women suffered from chronic pain, compared to 14% of men. This gender discrepancy, however, seemed to be linked primarily to differences in age, income, and education between adult men and women in this large sample. Age, income, depression and functional interference with activities were strongly associated with chronic pain in general. No gender differences were found in the intensity of pain experienced. Ethnicity was not strongly associated with chronic pain prevalence, although Asians were the group with the highest chronic pain prevalence in the over-65 age group and Aboriginal Canadians had the highest prevalence in the under-65 age group. DATA GAPS AND RECOMMENDATIONS: Current gaps in our knowledge include the types of chronic pain women experience, their impact on domestic responsibilities and parenting and health care utilization patterns of women with chronic pain. Data sources such as provincial databases of billing claims may be useful in the future to enrich our knowledge of health care utilization and analgesic medication use. Enhanced surveillance, assessment, and early identification of pain disorders are recommended to improve outcomes. Considering current demographic patterns toward an older population, there is also some urgency to the development of patient education and self-management programs.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.017
GPT teacher head0.286
Teacher spread0.269 · 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
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

Citations102
Published2004
Admission routes2
Has abstractyes

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