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Legislative Barriers and Legislative Changes for Physical Therapy During the Opioid Crisis in the US and Canada

2022· article· en· W7112305924 sur OpenAlexaboutno aff

Notice bibliographique

RevueDigital Access to Scholarship at Harvard (DASH) (Harvard University) · 2022
Typearticle
Langueen
DomaineHealth Professions
ThématiqueNursing Roles and Practices
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLegislatureLegislationOpioidInclusion (mineral)PoliticsHealth careAddiction
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The opioid crisis has led to tens of thousands of deaths over the last couple of decades, most notably in the United States (US) and Canada. While the opioid problem may have begun in the US, it quickly crossed borders and is now a global health issue. This is an ongoing crisis resulting in the search for and implementation of solutions for preventing and treating addiction to these drugs. Physical therapy is one such treatment. The profession’s focus on pain management, improvement of quality of life, and the patient’s active participation in their own treatment without the use of medication is vital for improving pain treatment and reducing the need for opioids. Despite the profession’s focus on pain management, the reason opioid prescribing became excessive, there has been little inclusion of physical therapy in treatment programs and few law changes to improve access to their services throughout the opioid crisis. The case studies in this research focus on Ontario, Canada and Ohio, US which were chosen because of similarities in the demographics between the two regions as well as similar law changes that will help assess how the healthcare and political system affected the barriers presented to the physical therapy profession in each region. A comparison was conducted of the two most recent law changes for physical therapy in each respective region: the 1991 Physiotherapy Act and the 2009 revision of said Act in Ontario; and the 2004 and 2019 revisions to the Ohio physical therapy laws. The comparison of the laws within each distinct region will add to existing knowledge of barriers to physical therapy by discovering what barriers exist for the physical therapy profession at the legislative level and how they have changed during the opioid crisis. Interviews were conducted with physical therapists in Ohio that had varying experience with legislation. Additionally, one interview with a member of the College of Physiotherapists of Ontario was also conducted. In addition to interviews, an examination of other primary sources included the proposed laws at various stages of the process; government reports; official transcripts for debates and formal submissions to legislative committees in Ontario; and recordings of legislative sessions in Ohio. Secondary sources consisted of journal articles; academic books; newspaper articles; and news releases and reports from the Ohio Physical Therapy Association, the Ohio State Medical Association, Ontario Physiotherapy Association, and College of Physiotherapists of Ontario. The healthcare system in which a health profession exists has a significant impact on the barriers they face for legislative change. Physicians had greater influence on legislation for physical therapy in Ohio and used that influence to block proposed law changes for physical therapy. Based on the comparison between the process in both regions, it was determined to be mostly due to the designation of physical therapy as a specialty care versus primary care and the differences in documentation of arguments. Ontario uses formal written submissions for arguments and considers physical therapists primary care, while in Ohio unrecorded meetings are the means of discussion and physical therapy is designated a specialty care. These two factors, specialty care and unrecorded arguments, create an environment in which physical therapists are unable to gain the political influence necessary to reduce barriers for physical therapy services.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,038
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,214
Score d'incertitude au seuil0,912

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,038
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,004
Études des sciences et des technologies0,0250,009
Communication savante0,0100,003
Science ouverte0,0030,005
Intégrité de la recherche0,0030,007
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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.

Tête enseignante Opus0,037
Tête enseignante GPT0,325
Écart entre enseignants0,288 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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