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Record W4387745366 · doi:10.2147/jpr.s413450

Perceptions and Preoccupations of Patients and Physicians Regarding Use of Medical Cannabis as an Intervention Against Chronic Musculoskeletal Pain: Results from a Qualitative Study

2023· article· en· W4387745366 on OpenAlexafffundabout
Lise Poisblaud, Edeltraut Kröger, Nathalie Jauvin, Julie Pelletier-Jacob, Richard E. Bélanger, Guillaume Foldes‐Busque, Michèle Aubin, Pierre Pluye, Laurence Guillaumie, Malek Amiri, Pierre Dagenais, Clermont E. Dionne

Bibliographic record

VenueJournal of Pain Research · 2023
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversité de SherbrookeUniversité LavalCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleInstitut National de Santé Publique du QuébecCentres Intégré Universitaires de Santé et de Services SociauxCentre hospitalier de l'Université LavalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre de Santé et de Services Sociaux de la Vieille-CapitaleMcGill UniversityCentre intégré de santé et de services sociaux de Chaudière-AppalachesCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean
FundersCanadian Institutes of Health Research
KeywordsMedicineCannabisQualitative researchTheory of planned behaviorPsychological interventionIntervention (counseling)Family medicineChronic painPsychiatry

Abstract

fetched live from OpenAlex

Objective: Explore perceptions and preoccupations regarding use of medical cannabis against chronic musculoskeletal pain, among patients and physicians. Design: Qualitative study using interviews with patients and physicians, based on the Theory of Planned Behavior (TPB). Setting: The study was conducted in Quebec, Canada, in spring 2020. Subjects: We included 27 adult patients and 11 physicians (GPs, anesthesiologists, psychiatrists, and a rheumatologist); the mean age of patients was 48.2 years; 59.3% of patients and 36.4% of physicians were women; 59.3% of patients used no medical cannabis at the time of study; 45.5% of physicians had never authorized it. Methods: Semi-structured interviews were conducted, transcribed and for the qualitative analysis codes were developed in a hybrid, inductive and deductive approach. Guided by the TPB, facilitators and barriers, perceived benefits and harms, and perceived norms that may influence cannabis use or authorization were documented. Results: Although medical cannabis is an interesting avenue for the relief of chronic musculoskeletal pain, doctors and patients agreed that it remained a last line option, due to the lack of scientific evidence regarding its safety and efficacy. The norms surrounding medical cannabis also play an important role in the social and professional acceptance of this therapeutic option. Conclusion: Medical cannabis is seen as a last line option among interventions in the management of chronic pain, and attitudes and prior experiences play a role in the decision to use it. Study results may contribute to improved shared decision making between patients and physicians regarding this option. Plain Language Summary: Little is known about the motivations, perceptions, and preoccupations of patients with chronic pain and their physicians regarding the use of medical cannabis against chronic pain. A qualitative study was done on the attitudes and perceptions of these patients and their physicians. We performed semi-structured interviews, guided by the Theory of Planned Behavior, with chronic pain patients and with physicians treating such patients. Results indicate that both patients and physicians consider medical cannabis as a last line therapeutic option against chronic pain. Also, both groups expressed a need for stronger evidence on the effectiveness and safety of medical cannabis, as well as for more and clearer guidance on when and how to use this additional option in the treatment of chronic pain. Keywords: medical cannabis, theory of planned behavior, chronic musculoskeletal pain, qualitative study, lack of knowledge, stigma

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)0.021
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.014
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 integrity0.0000.001
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 head0.077
GPT teacher head0.466
Teacher spread0.389 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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