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Record W3018758808 · doi:10.2196/19360

Advancing E-Mental Health in Canada: Report From a Multistakeholder Meeting

2020· editorial· en· W3018758808 on OpenAlexaffvenueabout
Gillian Strudwick, Danielle Impey, John Torous, Reinhard Michael Krausz, David Wiljer

Bibliographic record

VenueJMIR Mental Health · 2020
Typeeditorial
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity Health NetworkUniversity of British ColumbiaMental Health Commission of CanadaCentre for Addiction and Mental Health
Fundersnot available
KeywordsMental healthWorkforceHealth carePublic relationsNursingDigital healthMedicinePolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

The need for e-mental health (electronic mental health) services in Canada is significant. The current mental health care delivery models primarily require people to access services in person with a health professional. Given the large number of people requiring mental health care in Canada, this model of care delivery is not sufficient in its current form. E-mental health technologies may offer an important solution to the problem. This topic was discussed in greater depth at the 9th Annual Canadian E-Mental Health Conference held in Toronto, Canada. Themes that emerged from the discussions at the conference include (1) the importance of trust, transparency, human centeredness, and compassion in the development and delivery of digital mental health technologies; (2) an emphasis on equity, diversity, inclusion, and access when implementing e-mental health services; (3) the need to ensure that the mental health workforce is able to engage in a digital way of working; and (4) co-production of e-mental health services among a diverse stakeholder group becoming the standard way of working.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.392
Teacher spread0.364 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations25
Published2020
Admission routes3
Has abstractyes

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