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Record W2593020508 · doi:10.1139/facets-2016-0016

Applying a Bauhaus design approach to conceptualize an integrated system of mental health care: Lessons from a large urban hospital

2016· article· en· W2593020508 on OpenAlexaffvenueabout
Thomas Ungar, Marlene Taube‐Schiff, Vicky Stergiopoulos

Bibliographic record

VenueFACETS · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsSt. Michael's HospitalNorth York General HospitalUniversity of Toronto
Fundersnot available
KeywordsMental healthHealth careAmbulatory careNursingTriageConceptual frameworkMedicinePsychologyMedical emergencySociologyPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

We have applied a Bauhaus design lens to inform a visual conceptual framework for a rational mental health care system. We believe that Canada’s healthcare system can often be fragmented and does not always allow for service delivery to easily meet patient care needs. Within our proposed framework, the form of services provided follows patient- and healthcare-centred needs. The framework is also informed by the ethics and values of social responsibility, population health, and principles of quality of care. We review evidence for this framework (based on need, acuity, risk, service intensity, and provider level) and describe patient care pathways from intake/triage to three patient-centred tiers of care: (1) primary care (low needs), (2) acute ambulatory transitional care (moderate needs), and (3) acute hospital and complex care (high needs). Within each tier, various models of care are organized from low to high service intensity as informed by reports from the British Columbia Ministry of Health and the World Health Organization. We hope that our model may help to better conceptualize and organize our mental health care system and help providers clarify roles, responsibilities, and accountabilities to improve quality of care.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.053
GPT teacher head0.292
Teacher spread0.239 · 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 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

Citations3
Published2016
Admission routes3
Has abstractyes

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