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Record W2170819765 · doi:10.1007/s11469-012-9418-x

Partnerships Among Canadian Agencies Serving Women with Substance Abuse Issues and Their Children

2013· article· en· W2170819765 on OpenAlexafffundabout
Wendy Sword, Alison Niccols, Reza Yousefi‐Nooraie, Maureen Dobbins, Ellen L. Lipman, Patrick Smith

Bibliographic record

VenueInternational Journal of Mental Health and Addiction · 2013
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of British ColumbiaHamilton Health SciencesMcMaster University
FundersCanadian Institutes of Health Research
KeywordsHealth psychologyPublic healthSubstance abuseSubstance abuse treatmentRehabilitationSubstance useSubstance abuse preventionPsychologyEnvironmental healthPsychiatryMedicineNursing

Abstract

fetched live from OpenAlex

Women with substance use issues and their children have unique needs that are best met through collaborative and coordinated service delivery offered by a variety of agencies. However, in Canada and elsewhere, services tend to be fragmented and fail to address children's needs. This study aimed to describe the partnership patterns, activities, and qualities among Canadian agencies serving women with addictions and to determine predictors of partnerships. We found that a number of partnerships exist, and that the extent and characteristics of these partnerships vary. Agency responsiveness to clients was predictive of sending referrals whereas friendliness predicted joint programming and consultation. Four central agencies played key linkage roles. Efforts should be made to build on the social capital inherent in these agencies to strengthen existing networks, further develop linkages to improve service delivery, and promote evidence-informed practice in a field where there is an identified research-practice gap.

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)0.006
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: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0110.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.075
GPT teacher head0.343
Teacher spread0.268 · 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

Citations18
Published2013
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Mental Health and AddictionSame topicMental Health and Patient InvolvementFrench-language works237,207