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Record W3203446345 · doi:10.7202/1081518ar

Efficacité des applications mobiles et des messages textes comme intervention en cybersanté mentale pour les 3 blessures de stress opérationnel les plus fréquentes chez le personnel de la sécurité publique : une recension-cadre

2021· article· fr· W3203446345 on OpenAlexaffvenueabout
Florence Ménard, Isabelle Ouellet‐Morin, Stéphane Guay

Bibliographic record

VenueSanté mentale au Québec · 2021
Typearticle
Languagefr
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversité de MontréalInstitut universitaire en santé mentale de MontréalInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsHumanitiesPolitical scienceGynecologyPhilosophyMedicine

Abstract

fetched live from OpenAlex

Context The frequency of probable mental health diagnoses is much higher among public safety personnel (PSP) than in the general population, which can be explained in part by their operational duties. The most common operational stress injuries (OSI) among PSP in Canada are depression, post-traumatic stress disorder and generalized anxiety disorder. Because of their confidential and accessible nature, e-mental health interventions delivered via smartphones (mobile interventions) have great potential among PSP. The Mental Health Commission of Canada has proposed evaluative criteria for a comprehensive review of mobile interventions that goes beyond a scientific examination of the tool's effectiveness. Objectives The purpose of this framework review is to first identify the modalities and results of systematic reviews and meta-analyses that have examined the effectiveness of mobile interventions in reducing symptoms related to at least one OSI, primarily among PSPs. Second, we will evaluate the mobile interventions that have been studied with PSP using the evaluative criteria proposed by the Mental Health Commission of Canada to determine whether they can be recommended for use with PSPs. Methods Terms related to OSI, mobile technologies, and interventions were used in the Pubmed, PsycInfo, and Embase databases. Articles that measured the effectiveness of mobile interventions in relation to at least one OSI and from which the information for this review could be extracted were selected. Next, a review of the articles included in the selected reviews was conducted to identify studies conducted with a sample of PSP. Results The literature search did not identify any reviews that focused specifically on PSPs, so we had to expand our search to include adults in the general population. Nine articles met the inclusion criteria, which were published between 2016 and 2019. Overall, mobile interventions appear to significantly reduce symptoms of anxiety, depression, and post-traumatic stress. Two mobile interventions studied with PSP were identified, and these met the majority of the evaluative criteria. Conclusion The mobile interventions identified in the literature have great potential for the general population and for PSP. However, meta-analyses and systematic reviews report some important limitations such as heterogeneity between studies and a high drop-out rate. Future research on mobile interventions for PSPs would benefit from further investigation of aspects related to usability, user desirability, and security of personal information. Samples should also include a wider variety of public safety professions.

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)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.032
GPT teacher head0.366
Teacher spread0.333 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations3
Published2021
Admission routes3
Has abstractyes

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