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Record W1994613338 · doi:10.1080/08870440802094274

Fibromyalgia: Predicting openness to counselling referrals

2008· article· en· W1994613338 on OpenAlexaff
Natasha Ann Egeli, Peter D. MacMillan

Bibliographic record

VenuePsychology and Health · 2008
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsFibromyalgiaBiopsychosocial modelOpenness to experiencePsychosocialClinical psychologyDistressPsychologyEmotional distressContentmentMedicinePsychiatryAnxietyPsychotherapist

Abstract

fetched live from OpenAlex

Fibromyalgia (FM) is a chronic pain condition that can negatively impact on all aspects of patients' lives. The purpose of this study was: first, to explore the biopsychosocial factors that may contribute to adjustment to FM symptoms; second, to investigate how referrals to counselling related to patients' ratings of their relationship with their physicians; and, last, to examine if self-reports of illness distress, emotional problems, and practical problems can predict who will be open to counselling referrals. For this cross-sectional study, data from 190 people were collected through an online survey. Statistical analysis revealed that emotional problems reported were the best predictor of variance in illness distress and contentment scores. Further, results indicated that referrals made in accord with patients' perceptions that they would benefit from counselling may have a positive influence on how patients rate their relationships with their physicians. Finally, self-reported scores of illness distress, number of emotional problems, and number of practical problems accurately predicted who would be open to counselling referrals in 67% of cases. Research results provide support for addressing emotional issues to facilitate adjustment to FM symptoms, and for use of psychosocial measures to determine when patients with FM will be open to counselling referrals.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.453

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 head0.154
GPT teacher head0.437
Teacher spread0.284 · 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

Citations4
Published2008
Admission routes1
Has abstractyes

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