MétaCan
Menu
Back to cohort
Record W2081558423 · doi:10.1258/jtt.2007.070815

Mental health services for children and youth: a survey of physicians' knowledge, attitudes and use of telehealth services

2008· article· en· W2081558423 on OpenAlexaffabout
Paula Cloutier, Mario Cappelli, J. Elizabeth Glennie, Christian Keresztes

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2008
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsOntario Stroke NetworkChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsTelehealthMental healthTelemedicineMedicineTelepsychiatryFamily medicineHealth servicesPsychologyNursingPsychiatryHealth careEnvironmental health

Abstract

fetched live from OpenAlex

Rural physicians in Ontario, whose practice included children, were surveyed on their awareness, attitudes and use of telemental health services for children and young people in their region. Of 95 rural physicians, 70 completed and returned the telehealth section of the survey (74% response rate). The survey comprised 14 questions. Only 27% of responders were aware of the available videoconferencing services. The proportion of physicians who reported having referred patients for the various mental health services through videoconferencing was 0-24%. The proportion of physicians who reported that they would refer patients through videoconferencing was 55-92%. Reduced travel time and care provided closer to home were seen as the primary benefits of referring patients to mental health services through videoconferencing. Unclear referral patterns and technology compromises were seen as limitations of referring patients to videoconferencing. Access to rural populations and improved access to patients were seen as benefits to practice, and undeveloped remuneration procedures as the primary limitation. Promotion may be important to successful implementation of telemental health services for children and young people.

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 score0.048
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.039
GPT teacher head0.349
Teacher spread0.310 · 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

Citations24
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Telemedicine and TelecareSame topicTelemedicine and Telehealth ImplementationFrench-language works237,207