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Record W2105286514 · doi:10.1155/2011/326307

Improving the Management of Late-Life Depression in Primary Care: Barriers and Facilitators

2011· article· en· W2105286514 on OpenAlexafffundabout
Tamara Sussman, Mark J. Yaffe⃰, Jane McCusker, David M. Parry, Maida Sewitch, Lisa Van Bussel, Ilyan Ferrer

Bibliographic record

VenueDepression Research and Treatment · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsWestern UniversityMcGill University Health CentreSt Mary's Hospital CentreSt Joseph's Health CentreMcGill University
FundersCanadian Institutes of Health Research
KeywordsMedicinePrimary careQualitative researchNursingMental healthPrimary health careRank (graph theory)Process (computing)Value (mathematics)Depression (economics)Family medicinePsychiatry

Abstract

fetched live from OpenAlex

The objectives of this study were to elicit Canadian health professionals' views on the barriers to identifying and treating late-life depression in primary care settings and on the solutions felt to be most important and feasible to implement. A consensus development process was used to generate, rank, and discuss solutions. Twenty-three health professionals participated in the consensus process. Results were analysed using quantitative and qualitative methods. Participants generated 12 solutions. One solution, developing mechanisms to increase family physicians' awareness of resources, was highly ranked for importance and feasibility by most participants. Another solution, providing family physicians with direct mental health support, was highly ranked as important but not as feasible by most participants. Deliberations emphasized the importance of case specific, as needed support based on the principles of shared care. The results suggest that practitioners highly value collaborative care but question the feasibility of implementing these principles in current Canadian primary care contexts.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.730

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.101
GPT teacher head0.434
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 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

Citations9
Published2011
Admission routes3
Has abstractyes

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