Cannabis use among workers with work-related injuries and illnesses: results from a cross-sectional study of workers’ compensation claimants in Ontario, Canada
Notice bibliographique
Résumé
OBJECTIVES: Little is known about how workers use cannabis following a work-related injury/illness, including whether they receive clinical guidance. The objective was to compare characteristics of workers using and not using cannabis after a work-related injury/illness and describe use patterns. DESIGN: Cross-sectional study. SETTING AND PARTICIPANTS: Workers who experienced a work-related physical injury/illness resulting in one or more days of lost time compensated by the workers' compensation authority in Ontario, Canada (n=1196). METHODS: Participants were interviewed 18 or 36 months after their injury/illness. Participants were asked about their past-year cannabis use, including whether use was for the treatment of their work-related condition. Sociodemographic, work and health characteristics were compared across cannabis groups: no past-year use; use for the work-related condition; use unrelated to the work-related condition. Cannabis use reasons, patterns, perceived impact and healthcare provider engagement were described. RESULTS: In total, 27.4% of the sample reported using cannabis (14.1% for their work-related condition). Workers using cannabis for their condition were less likely to be working (58.0%) and more likely to have quite a bit/extreme pain interference (48.5%), psychological distress (26.0%) and sleep problems most/all the time (62.1%) compared with those not using cannabis (74.3%, 26.3%, 12.0% and 38.0%, respectively) and those using cannabis for other reasons (74.2%, 19.5%, 12.0% and 37.1%, respectively) (all p<0.0001). No significant differences were observed in medical authorisations for use among those using cannabis for their condition (20.4%) or unrelated to their condition (15.7%) (p=0.3021). Healthcare provider guidance was more common among those using cannabis for their condition (32.7%) compared with those using for other reasons (17.1%) (p=0.0024); however, two-thirds of this group did not receive guidance. CONCLUSIONS: Cannabis may be used to manage the consequences of work-related injuries/illnesses, yet most do not receive clinical guidance. It is important that healthcare providers speak with injured workers about their cannabis use.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».