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Record W2731595541 · doi:10.1080/24740527.2017.1316173

The suffering of chronic pain patients on a wait list: Are they amenable to narrative therapy?

2017· article· en· W2731595541 on OpenAlexaff
Eloise Carr, Graham McCaffrey, Mia Ortiz

Bibliographic record

VenueCanadian Journal of Pain · 2017
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChronic painPsychosocialThematic analysisIntervention (counseling)Coping (psychology)MedicineNarrativePsychologyPhysical therapyPsychotherapistQualitative researchClinical psychologyPsychiatrySociology

Abstract

fetched live from OpenAlex

Background: Chronic pain affects one in five Canadians. People with chronic pain frequently experience loss in their lives related to work, relationships, and their independence. They may be referred to a chronic pain program, which aims to strengthen coping through medical intervention and self-management skills. Data suggest that, even when individuals begin their pain program, many feel overwhelmed and do not continue.Aims: The aim of this study was to conduct a needs assessment to explore the acceptability and feasibility of developing a psychosocial intervention, narrative therapy (NT), to address loss for chronic pain patients on the wait list of a chronic pain program.Methods: Two focus groups were conducted with ten patients who had experienced being on a wait list for a provincial chronic pain management program (CPMP). Transcribed interviews were subjected to thematic and interpretive analysis.Results: Two major themes emerged from the analysis: loss of identity and sharing a story of chronic pain. All patients were enthusiastic toward an NT intervention, although individual preferences differed regarding mode of delivery.Conclusions: Loss is a significant part of the chronic pain experience. NT seems to be an acceptable intervention to address loss for patients on the wait list for a chronic pain 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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.274
Teacher spread0.254 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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