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Topical issues of improving the quality of online services in the field of child mental health

2022· article· en· W4295528381 on OpenAlexaff
М А Бебчук, Oleg Z. Khairetdinov, O. Shchedrinskaya, Rafael G. Saakov

Bibliographic record

VenueHealth Care of the Russian Federation · 2022
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsMental healthConfidentialityFlexibility (engineering)TelemedicineTelehealthQuality (philosophy)The InternetPsychologyMedicineMedical educationHealth careApplied psychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Introduction. Thery has been conducted study on the risks and benefits of remote psychiatric and psychological services provided through video-internet communication. Purpose. To explore and manage the quality of telehealth services in mental health, including such aspects as legal, ethical, clinical, social risks relevant for online counseling. Methods. The attitude towards online counseling format among clients and mental health care providers (psychiatrists, psychologists, clinical counselors) was explored with an online survey. The study included two stages with two hundred thirty two and 279 respondents. Results. The summary of benefits included cost-efficiency of the online services, flexibility in scheduling, availability of the services for people from remote communities and with mobility issues, epidemiological benefits (accesses to services during COVID-19 pandemic). Limitations and potential risks included decrease in the quality of communication and interaction, which can result in misdiagnosis or incorrect interpretation of various tests and questionnaires, miscommunication, change in the level of motivation, age-related limitations. Additionally, there are risk factors related to technological side - e.g. potential risks for confidentiality and personal information disclosure. Limitations. The limitations of the study are related to the method of obtaining information through an anonymous electronic survey and the relatively small number of respondents. Conclusions. The COVID-19 epidemic has significantly accelerated the development of telemedicine. The benefits and opportunities of online sessions outweigh the risks and limitations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.029
GPT teacher head0.431
Teacher spread0.402 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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