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Record W4388565188 · doi:10.1080/07347324.2023.2278532

Recommending Collegiate Recovery Programs to Institutes of Higher Education in Ireland

2023· article· en· W4388565188 on OpenAlexaboutno aff
Declan G. Murphy

Bibliographic record

VenueAlcoholism Treatment Quarterly · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsIrishMedical educationAddictionHigher educationPopulationMedicinePerspective (graphical)PsychologyFamily medicinePolitical sciencePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

In 2022, a national study titled The DUHEI (Drug Use in Higher Educationin Ireland) was conducted to determine the prevalence and problem of drug use among students in institutes of higher education. The findings of the DUHEI revealed that drug use is prevalent among college students in Ireland, with over half reporting they use or hadused drugs in the past.The study also uncovered a subpopulation of students who have recovered from a previous problem with drug or alcohol use in thepast, known as students in recovery. These students were identifiedas a unique and vulnerable population in need of specific support on college campuses. The authors of the report recommend the establishment of collegiate recovery programs in institutes of higher education in Ireland to support these students in active recovery from addiction. Collegiate recovery programs are well established in the U.S and are emerging in the UK and Canada. This perspective article echoes, and extends upon the DUHEI recommendationto implement CRPs in Ireland, by highlighting the evidence base which demonstrates the efficacy of collegiate recovery programs. Itis important that Irish colleges and Universities follow international best practice by establishing collegiate recovery program.

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)0.000
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 score0.726
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.032
GPT teacher head0.331
Teacher spread0.299 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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