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Record W172957689 · doi:10.24095/hpcdp.29.4.03

Navigating the health care system: perceptions of patients with chronic pain

2009· article· en· W172957689 on OpenAlexaffvenue
A. L. Dewar, Kathy Gregg, Marc White, Janice Lander

Bibliographic record

VenueChronic diseases in Canada · 2009
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsMedicineChronic painOptimismQualitative researchHealth carePerceptionNursingFamily medicinePhysical therapyPsychotherapistPsychology

Abstract

fetched live from OpenAlex

A new framework is needed for patients with chronic pain and their primary care physicians that acknowledges the individual's experiences and provides evidence-informed education and better linkages to community-based resources. This study describes the experience of 19 chronic-pain sufferers who seek relief via the health care system. Their experiences were recorded through in-depth semistructured interviews and analyzed through qualitative methods. The participants reported early optimism, then disillusionment, and finally acceptance of living with chronic pain. Both individuals with chronic pain and their health care professionals need evidence-informed resources and information on best practices to assist them to manage pain. Empathetic communication between health care professionals and individuals with chronic pain is crucial because insensitive communication negatively affects the individual, reduces treatment compliance and increases health care utilization.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.269
Teacher spread0.265 · 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 designQualitative
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

Citations35
Published2009
Admission routes2
Has abstractyes

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