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Record W4367844996 · doi:10.1108/mhsi-04-2023-0040

The experience of launching a psychological hotline across 21 countries to support Ukrainians in wartime

2023· article· en· W4367844996 on OpenAlexaff
Valeriia Palii, Mariana Velykodna, Marcio Pereira, Rosaleen McElvaney, Sam Bernard, Vitalii Klymchuk, Oleg Burlachuk, Alexander A. Lupis, Nadiia Diatel, Jane L. Ireland, Kimberley McNeill, Janina Scarlet, Ana L. Jaramillo‐Sierra, Bassam Khoury, Diana Rocio Sánchez Munar, Sarah L. Hedlund, Tara Flanagan, Jeanne LeBlanc, Diana María Agudelo Vélez, Yvonne Gómez

Bibliographic record

VenueMental Health and Social Inclusion · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsHotlineMental healthPublic healthWork (physics)MedicinePublic relationsPsychologyNursingPolitical sciencePsychiatryEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Purpose This paper aims to discuss current work and further steps of the psychological hotline launched by the National Psychological Association of Ukraine (NPA), along with a call for action to mental health professionals worldwide. Design/methodology/approach This paper describes the training and support of the NPA’s hotline staff as well as reflections on the hotline’s work from June 2022 to April 2023. Findings With broad international support, the NPA’s psychological hotline currently operates in 21 countries providing psychological assistance and referrals to other service providers within Ukraine and abroad. The authors propose further steps of its work, including international collaboration. Originality/value Providing citizens of Ukraine with broad public access to evidence-based remote psychological support through NPA’s hotlines is a high priority considering the war’s negative impact on mental health diverse and the limited capacity of the state mental health system.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.047
GPT teacher head0.461
Teacher spread0.413 · 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 designQualitative
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

Citations17
Published2023
Admission routes1
Has abstractyes

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