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Record W2019120869 · doi:10.1080/16501970310019142

Evaluation of changes in occupational performance among patients in a pain management program

2004· article· en· W2019120869 on OpenAlexaboutno aff
Elisabeth Persson, Marcelo Rivano‐Fischer, Mona Eklund

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialVitalityMedicineClinical psychologyPhysical therapyCompensation (psychology)PsychologyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: The aims of this study were to evaluate changes in occupational performance among chronic pain patients after a pain management program and to explore relationships between these changes and demographic and clinical factors, psychosocial functioning and psychological well-being. SUBJECTS: 188 consecutive patients were included. METHODS: Changes were registered by using Canadian Occupational Performance Measure, Multidimensional Pain Inventory and Psychological General Well-Being Index. RESULTS: There were statistically significant positive changes in occupational performance. Patients with sickness compensation had significantly higher changes in occupational performance than those without sickness compensation. The patients with a profile group as interpersonally distressed had statistically significant higher change scores on occupational performance than the adaptive coper group. Furthermore, increases in changes on general activity level, general health, and vitality and decreases in pain severity were associated with positive changes on perceived occupational performance and performance satisfaction. CONCLUSION: Changes in occupational performance, psychological well-being and psychosocial functioning seem all to be of relevance in the evaluation of pain management programs. Psychosocial profiles and sickness compensation has relevance for directions on changes in occupational performance, whereas other demographic and clinical factors do not.

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)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.184
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.018
GPT teacher head0.332
Teacher spread0.314 · 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

Citations39
Published2004
Admission routes1
Has abstractyes

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