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Record W7066573989

Harm Reduction in Ontario: Approach Opioid Crisis from the Spectrum of Governing the Commons

2019· dissertation· en· W7066573989 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Library Of The Commons Repository (Indiana University) · 2019
Typedissertation
Languageen
FieldArts and Humanities
TopicHistorical Studies in Science
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationPunitive damagesHarm reductionHarmPoliticsPrecautionary principleSafeguardingPandemicLaw enforcement
DOInot available

Abstract

fetched live from OpenAlex

"The increase in psychoactive drugs and its association with generating state profit creates many disagreements and prejudices towards harm reduction policies programs and practices, primarily to reduce adverse health, social and economic consequences of the use of legal and illegal psychoactive. 10 percent of these legal and illegal psychoactive drug users suffer from an opioid-related crisis and developed nations such as Canada, Australia, and the United States are left with untreated pain and lack in the appropriate use of opioids and other health-hazard risks of nonmedical use (Globenewswire, 2019). However, Canada remains to view the epoch crisis from political spectrum, re-implementing and redesigning the punitive laws and legislation of the drug consumption, exacerbating the drug-related deaths and victims, rather, to reduce the burden of the crisis effects on the local populations by adapting to the pandemic and resolving issues through provision of access to adequate resources, health-related treatments, and improve prevention sites (both in numbers and quality). It is further noted without intervention, there is expected to be 235 000 opioid-related deaths (both from prescript and illicit drug consumption) from 2016 to 2020, and this number is to be increased to 500 000 by 2025 (Allison, Keith, Margaret, 2018). To illustrate the opioid crisis, it is important to address the epidemic from determinants of social (and educational), etiology, and epidemiological grassroots rather a political problem of Canada. Furthermore, there are supporting objectives to direct why there is a need to utilize the drug's historical and current methodologies to overcome the crisis because there exists mass research and policies over opioid overdose, but constraints active planning, designing and implementing of health systems strategies (eg, needs, usage, risk of opioid-related preventions). First, it studies the need for reimplementation of opioid overdose crisis in its published and existing harm reduction policies and practices/ programs. Second, it supports crisis management by analyzing the governance of commons resources of the Canadian healthcare systems prior to health determinants inequalities and access to prevention facilities. Third, opioid crisis needs to be scrutinized under the Canadian healthcare system both as a state governing and community-based governing resource in which both the allocation and utilization of healthcare facilities benefits (or deprives) from the access of the services to its local population. This is because the Canadian health sector subsidies from the same system which also facilitates the distribution of country’s national economic, education, and governmental institutions causing fluctuations in its management of commons servicing the prevention of national opioid crisis."

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.116
Threshold uncertainty score0.843

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0240.009
Scholarly communication0.0070.003
Open science0.0020.009
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0130.001

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 head0.015
GPT teacher head0.165
Teacher spread0.150 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2019
Admission routes1
Has abstractyes

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