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Predicting Readiness to Attend an Interdisciplinary Pain Management Program: What’s better for Clinical Decision-Making? Clinical Judgment or a Patient Self- Report Questionnaire?

2017· article· en· W2779403076 on OpenAlexaffabout
Hapidou EG, G Michael

Bibliographic record

VenueAustin Journal of Anesthesia and Analgesia · 2017
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsClinical decision makingPsychologyClinical judgmentMedical educationPain managementMedicineFamily medicineMedical physicsPhysical therapy

Abstract

fetched live from OpenAlex

Background: Chronic Pain (CP) can have a substantial negative impact on one's life.Patients often seek Pain Management Programs (PMPs) as a means to treat their CP condition.The Michael G. DeGroote Pain Clinic, located in Hamilton, Ontario is a PMP that admits patients based on a variety of clinically important factors.Patients assessed are either recommended or not recommended into the program after consideration of these factors.Aims: The objective of this study was to examine if readiness, as assessed by the Pain Stages of Change Questionnaire (PSOCQ), is associated with a clinical judgment of readiness in recommending a person into a PMP.Additionally, to investigate whether PSOCQ scores or clinician judgment predicted readiness to attend a PMP.Methods: One-hundred and eight people were approached and recruited to this study.The 108 patients referred to the PMP in Ontario, were either recommended or not recommended into a PMP after completing an initial assessment.Associations between clinician rating, recommendation status and PSOCQ subscale scores were analyzed using independent t-tests, Pearson Correlation, and Stepwise Regression.We hypothesize that readiness assessed by the PSOCQ would be associated with clinical judgment of readiness in recommending a person into the PMP but that clinical judgment would be superior in predicting readiness to attend a PMP rather than the PSOCQ scores.Results: Those recommended to the PMP had higher assessor ratings, lower pre-contemplation and higher contemplation scores.There were significant relationships between the clinician's rating, pre-contemplation, contemplation, and recommendation status.Stepwise regression methods revealed that while there may be benefit to using questionnaire measures of readiness to change, clinical judgment was the best predictor for recommendation into the PMP.Conclusions: Clinical judgment in the initial assessment process was superior in clinical decision-making regarding a patient's readiness to attend a PMP, as compared to a self-report questionnaire.

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)0.007
metaresearch head score (Gemma)0.032
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 score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.035
GPT teacher head0.432
Teacher spread0.397 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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