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Record W7112305924

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

2022· article· en· W7112305924 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Access to Scholarship at Harvard (DASH) (Harvard University) · 2022
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsLegislatureLegislationOpioidInclusion (mineral)PoliticsHealth careAddiction
DOInot available

Abstract

fetched live from 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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.002
Open science0.0010.001
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 head0.037
GPT teacher head0.325
Teacher spread0.288 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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