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Record W2914508493 · doi:10.1177/0840470418818583

E-mental health: Promising advancements in policy, research, and practice

2019· review· en· W2914508493 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

VenueHealthcare Management Forum · 2019
Typereview
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversité de MontréalDouglas Mental Health University Institute
Fundersnot available
KeywordsMental healthThe InternetHealth careScale (ratio)Social mediaPublic relationsField (mathematics)Health policyBusinessPsychologyMedicineNursingPolitical scienceComputer sciencePublic healthPsychiatryWorld Wide WebGeography

Abstract

fetched live from OpenAlex

The increasing need for mental health services in the population is posing significant challenges for the health system. It is therefore important to identify new approaches to delivering care that are sustainable and scalable in terms of reach and impact. E-mental health is one approach that shows promise in addressing the treatment gap in mental healthcare. E-mental health involves leveraging the Internet and related technologies such as smartphone apps, web sites, and social media to deliver mental health services. Over the past decade, this field has made significant advancements in Canada and internationally. In this article, the author introduces the e-mental health field and provides an overview of promising Canadian developments in relation to policy, research, and practice. In addition, the article discusses some of the challenges facing the wide-scale implementation of e-mental health and identifies priority areas of focus for health leaders to advance the field.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.336
GPT teacher head0.611
Teacher spread0.276 · 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