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Record W3184234293 · doi:10.1111/eip.13196

Healthcare providers' perceptions of virtual‐care with children's mental health in a pandemic: A hospital and community perspective

2021· article· en· W3184234293 on OpenAlexafffund
Erin Romanchych, Riddhi Desai, Christina Bartha, Neill Carson, M. Korenblum, Suneeta Monga

Bibliographic record

VenueEarly Intervention in Psychiatry · 2021
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of TorontoSickKids FoundationHospital for Sick Children
FundersHospital for Sick Children
KeywordsMental healthPandemicHealth careNursingPerceptionMedicinePerspective (graphical)Virtual communityMental healthcarePsychologyCoronavirus disease 2019 (COVID-19)PsychiatryDiseaseThe Internet

Abstract

fetched live from OpenAlex

AIM: The purpose of the present study was to explore the experiences of a diverse group of mental health clinicians both in hospital and in the community, who were required to rapidly adopt virtual-care practices in the delivery of mental health services to children, adolescents, and their families. METHODS: Mental health clinicians (N = 117) completed the Clinician Virtual-Care Experience Survey assessing the following domains: ease of technology use, client/patient-provider interaction quality, and clinician wellbeing. RESULTS: Although over 70% of clinicians had not used virtual-care to deliver mental health services prior to the Coronavirus Disease 2019 pandemic, more than 80% felt it was easy to operate the virtual platforms. Clinicians were divided in their perceptions of the effectiveness of virtual-care, with only 42% reporting that they felt they were as effective in delivering healthcare services virtually as compared to in-person. Virtual-care was described as being more effective for specific populations, while challenges were described in building rapport and when delivering difficult or unexpected feedback. CONCLUSIONS: Clinicians felt there were some benefits of adopting virtual-care practices, while challenges were also identified. Understanding of the impact of virtual-care on service providers is essential in order to strengthen mental healthcare for children, adolescents, and their families even beyond the pandemic.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.994

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 integrity0.0000.001
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 head0.013
GPT teacher head0.352
Teacher spread0.339 · 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

Citations20
Published2021
Admission routes2
Has abstractyes

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