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Record W2312424527 · doi:10.1177/1084822315572114

Bridging Silos

2015· article· en· W2312424527 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHome Health Care Management & Practice · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsInstitute of Health Services and Policy ResearchVancouver Coastal Health Research InstituteUniversity of British Columbia
Fundersnot available
KeywordsFocus groupIntervention (counseling)Poisson regressionMedicineBridging (networking)NursingPsychologyFamily medicineComputer science

Abstract

fetched live from OpenAlex

Canadian family physicians (FPs) and home health staff (HHS) experience significant barriers to collaboration regarding patients whose needs are complex. This study used mixed methods to examine whether pre-scheduled, structured audio-conferencing could improve patient-related collaboration between physicians and HHS. The number of shared patients and contacts was collected across three phases: baseline, pre-intervention, and intervention. Interviews with FPs and focus groups with HHS were conducted post-intervention. Mixed effects Poisson regressions for count data, and content analysis for interview and focus group data, were used. No statistically significant “intervention” effect was observed in either the number of shared patients or the average patient contacts. Physicians participating in at least one audio-conference had a lower patient contact rate than the rest of the intervention group and controls. Qualitative data suggested that audio-conferences led to fewer contacts due to more efficient communication.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.812
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.003

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 head0.050
GPT teacher head0.490
Teacher spread0.441 · 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