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Record W3156813660 · doi:10.2196/28055

Multimodule Web-Based COVID-19 Anxiety and Stress Resilience Training (COAST): Single-Cohort Feasibility Study With First Responders

2021· article· en· W3156813660 on OpenAlexvenueno aff
Janna Marie Heyen, Noé Weigl, Mario Müller, Stefan Müller, Urs Eberle, Andrei Manoliu, Stefan Vetter, Adam D. Brown, Thomas Berger, Birgit Kleim

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsCoronavirus disease 2019 (COVID-19)AnxietyResilience (materials science)Psychological resilienceStress (linguistics)Cohort2019-20 coronavirus outbreakPsychologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicMedicineClinical psychologyInternal medicineVirologyPsychiatryPsychotherapistMaterials scienceOutbreak

Abstract

fetched live from OpenAlex

BACKGROUND: Since the emergence of COVID-19, health care workers and first responders have been at a high risk for mental health symptoms owing to their exposure to the virus and increased work stress during the pandemic. Although interventions exist to address mental health issues following exposure to disasters, emergencies, and humanitarian crises, considerably less is known about web-based unguided interventions to help mitigate the negative impacts of such events. Additionally, in contexts in which emergencies reduce access to in-person care, remote forms of support are critical, yet there are limited studies on the use of such interventions. Evidence-based, easy-to-use, scalable interventions are direly needed for this population. OBJECTIVE: This study aimed to develop and test the feasibility of an unguided electronic mental health program, COVID-19 Anxiety and Stress Resilience Training (COAST), tailored to first responders and health care personnel, based on scientific evidence and empirically based techniques. METHODS: We developed COVID-19-specific training modules focusing on several domains that are previously reported as key to resilience and stress recovery: self-efficacy, mindfulness, sleep quality, and positive thinking. The program was made available to 702 first responders between May and August 2020, during the COVID-19 pandemic. Sociodemographic, work-, and COVID-19-related information was collected, and psychometric questionnaires were completed. We examined user acceptance and user activity, including module choice and participant feedback. RESULTS: In total, 52 of 702 (7%) first responders to whom we reached out used the program at least once. COAST use was independent of age, sex, or baseline levels of self-efficacy, mindful awareness, sleep quality, and positive thinking (for all, P>.39). First responders who had tested positive and those who had been quarantined were more likely to engage in the program. A click count analysis per module showed that participants used the self-efficacy and mindfulness modules most often, with 382 and 122 clicks, respectively, over 15 weeks. Overall, first responders expressed satisfaction with the program. CONCLUSIONS: Engagement of first responders in the multimodule web-based COAST program was feasible and the first responder cohort expressed overall satisfaction with the program. Those in more difficult circumstances, including those in quarantine and those who tested positive, may be more likely to engage in such programs. Further controlled studies could pave the way for efficacy studies and the development of additional modules, including just-in-time interventions or blended interventions combining individual use of an unguided self-help intervention, such as COAST, with subsequent individual psychotherapy for those who continue to experience stress and psychological symptoms.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.086
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.212
GPT teacher head0.499
Teacher spread0.287 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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