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Record W2411002958 · doi:10.1097/jan.0000000000000124

Health Promotion in an Opioid Treatment Program

2016· article· en· W2411002958 on OpenAlexfundno aff
Christine Gadbois, Elizabeth Chin, Lee Dalphonse

Bibliographic record

VenueJournal of Addictions Nursing · 2016
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersRegistered Nurses' Association of Ontario
KeywordsNursingMedicineGeneral partnershipHealth careReferralOpioid use disorderContext (archaeology)Health promotionPracticumFamily medicinePublic healthMedical educationOpioidBusiness

Abstract

fetched live from OpenAlex

Community assessment and review of the literature indicate that individuals supported in opioid treatment programs are at a significant disadvantage for access to preventative and primary healthcare. In addition, this population faces increased comorbidities and chronic disease. Finally, access to housing, nutritious food, and other social determinants of health is also a challenge for these individuals. This project, aimed at addressing healthcare disparities and improving health outcomes for the opioid treatment program client, was undertaken at a large, private, not-for-profit, community mental health center in an urban area. An education-practice partnership was created between the center and the local university's College of Nursing, which includes undergraduate and graduate programs. Working with administration, nurses, medical staff, and clinicians, the advanced practice nurse guided nursing practice change within the context of an interdisciplinary team to increase attention to clients' health needs. Outcomes included a more comprehensive nursing health assessment and increased attention to nursing care coordination. The partnership between the university and the facility continues with the goal of addressing clients' unmet healthcare needs and improving wellness via on-site intervention, referral, and education.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.232
GPT teacher head0.539
Teacher spread0.307 · 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 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

Citations4
Published2016
Admission routes1
Has abstractyes

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