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Record W2291945610 · doi:10.1007/s40122-016-0047-0

Learning to Manage Chronic Pain: The Patients’ Perspective

2016· article· en· W2291945610 on OpenAlexaff
Eleni G. Hapidou, Emily Horst

Bibliographic record

VenuePain and Therapy · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsHamilton Health SciencesMcMaster University
Fundersnot available
KeywordsReferralPromotion (chess)PsychologyMedical educationChronic painMental healthScale (ratio)Quality of life (healthcare)Perspective (graphical)Unit (ring theory)NursingMedicinePhysical therapyPsychiatryComputer science

Abstract

fetched live from OpenAlex

INTRODUCTION: The objective of the present study was to gain insight into patients' experiences in a 4-week interdisciplinary chronic pain management program by determining major themes from patients' written comments on exit questionnaires. METHODS: Upon completion of the program at the Chronic Pain Management Unit (CPMU), patients fill out program satisfaction (Pain Program Satisfaction Questionnaire) and evaluation of goal accomplishment (Self-Evaluation Scale) forms, sections of which are open-ended. Questionnaire data from 50 patients, admitted into the CPMU between May 2013 and December 2014, were randomly selected for this study. Written responses to open-ended sections were obtained. Comments were stratified by gender and coded using an inductive approach. Codes were grouped into categories which were further combined into several major themes. RESULTS: Six main themes extracted from comments were (1) impact of a strong interdisciplinary team, (2) learning to adapt in order to manage, (3) the Program as a stepping stone, (4) positive effects of a group effort, (5) improved mental health, and (6) benefits of the program. CONCLUSION: The results of this analysis reinforce the effectiveness of the interdisciplinary CPMU program at improving patients' quality of life. Findings may assist in the promotion of the program to stakeholders such as referral sources. The outcomes may also assist in the development of future programs that have similar goals. Concerns that arise within patients' comments may assist clinicians in this program to make adjustments such that all unique needs are met.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.143

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.254
Teacher spread0.247 · 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 designOther design
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

Citations12
Published2016
Admission routes1
Has abstractyes

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