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Record W3011502340 · doi:10.3138/ptc-2019-0026

Ontario Musculoskeletal Physiotherapists’ Attitudes toward and Beliefs about Managing Chronic Low Back Pain

2020· article· en· W3011502340 on OpenAlexaffvenueabout
Elizabeth Benny, Cathy Evans

Bibliographic record

VenuePhysiotherapy Canada · 2020
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicinePsychosocialPhysical therapyLow back painAlternative medicinePsychiatry

Abstract

fetched live from OpenAlex

Purpose: The purpose of this study was to explore Ontario musculoskeletal physiotherapists’ attitudes toward and beliefs about managing chronic low back pain (CLBP), and their biomedical (BM) and bio-psychosocial (BPS) treatment orientation. Method: Through a link in the Ontario Physiotherapy Association newsletter, we administered an electronic survey to registered physiotherapists via SurveyMonkey. We used a modified three-step Dillman approach to encourage participation. The questionnaire included the Pain Attitudes and Beliefs Scale for Physiotherapists (PABS–PT) measure, and demographic–practice items. Results: A total of 99 physiotherapists met the eligibility criteria and completed the PABS–PT (72.7% women; mean 17 years of experience). Respondents scored a mean of 26.98 (SD 7.69) on the BM sub-scale and 34.43 (SD 4.84) on the BPS sub-scale. Physiotherapists in public practice had a stronger BPS orientation (mean 36.52) than those in private practice (33.80; p = 0.01). Less experienced physiotherapists (<10 y) had a higher BM sub-scale score (mean 29.33) than more experienced physiotherapists (25.24, p = 0.013), and 78.8% of physiotherapists reported an awareness of clinical practice guidelines. Conclusions: Our preliminary findings suggest that Ontario physiotherapists’ attitudes and beliefs align with a BPS orientation. Future studies should explore the impact of education that promotes a BPS approach to the management of CLBP.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.008
GPT teacher head0.258
Teacher spread0.250 · 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.

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

Citations23
Published2020
Admission routes3
Has abstractyes

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