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Record W4220692827 · doi:10.1002/9781119701170.ch27

Pain catastrophizing and fear of movement: detection and intervention

2022· other· en· W4220692827 on OpenAlexaff
Catherine Paré, Michael Sullivan

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcGill University
Fundersnot available
KeywordsPain catastrophizingPsychological interventionPsychologyIntervention (counseling)Coping (psychology)Acceptance and commitment therapyClinical psychologyCognitionPhysical therapyChronic painMedicinePsychiatry

Abstract

fetched live from OpenAlex

Catastrophic thinking and fear of movement are two psychological variables that have been shown to be significant determinants of pain and disability associated with persistent pain conditions. This chapter briefly reviews what is currently known about the impact of pain catastrophizing and fear of movement on pain outcomes. It describes assessment techniques and intervention approaches for individuals who present with high levels of pain catastrophizing and fear of movement. Several instruments have been developed to assess pain catastrophizing. Considerable research on catastrophizing has used the Coping Strategies Questionnaire. Clinical interventions that have been shown to reduce levels of fear of movement include: education, reassurance and activity encouragement; graded exposure to feared activities; activity monitoring, progressive goal setting and graded activity; cognitive behavioral therapy techniques and acceptance and commitment therapy.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.006
GPT teacher head0.248
Teacher spread0.242 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2022
Admission routes1
Has abstractyes

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