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Record W3154000709 · doi:10.1080/08897077.2021.1892012

Successful Implementation of Managed Alcohol Programs in the San Francisco Bay Area during the Covid-19 Crisis

2021· article· en· W3154000709 on OpenAlexaboutno aff
Jessica T. Ristau, Nicky J. Mehtani, Seth Gomez, Michelle Nance, Devora Keller, Colleen S. Surlyn, Joanna Eveland, Shannon Smith‐Bernardin

Bibliographic record

VenueSubstance Abuse · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthIsolation (microbiology)Psychological interventionAlcohol use disorderIntervention (counseling)MedicineDocumentationMedical emergencyPsychologyNursingComputer scienceAlcohol

Abstract

fetched live from OpenAlex

Background The COVID-19 crisis presents new challenges and opportunities in managing alcohol use disorders, particularly for people unable to shelter in place due to homelessness or other reasons. Requiring abstinence for shelter engagement is impractical for many with severe alcohol use disorders and poses a modifiable barrier to self-isolation orders. Managed alcohol programs (MAPs) have successfully increased housing adherence for those with physical alcohol dependence in Canada, but to our knowledge, they have not been implemented in the United States. To avoid life-threatening alcohol withdrawal syndromes and to support adherence to COVID-19 self-isolation and quarantine orders, MAPs were piloted by the public health departments of San Francisco and Alameda counties. Development of MAPs We describe implementation of a first-in-the-nation alcohol use disorder intervention of a MAP that emerged at three public health isolation settings within San Francisco and Alameda counties in California. All three interventions utilized a similar process to develop the protocol and implement the MAP that included identification of champions for system-level advocacy and engagement of stakeholders. Implementation of MAPs We describe the creation and implementation of the distinct protocols. We provide examples of iterative changes to workflow processes and key lessons learned pertaining to protocol development, acceptability by stakeholders, alcohol procurement, documentation, and assessment. We discuss safety considerations, noting that there were no deaths or serious adverse events in any of the patients of the MAP during the 2-month implementation period. Conclusions MAP pilots have been implemented in the US to aid adherence to isolation and quarantine setting guidelines. Lessons learned provide a foundation for their expansion as a recognized public health intervention for individuals with severe alcohol use disorders who are unable to stabilize within existing care systems. Based on the success of MAP implementation, efforts are under way to investigate alcohol management in homeless populations more broadly.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.662

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.420
Teacher spread0.332 · 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.

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

Citations19
Published2021
Admission routes1
Has abstractyes

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