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Record W3116302779 · doi:10.1186/s43045-020-00075-4

Impact of COVID-19 on adolescents’ mental health: a systematic review

2020· review· en· W3116302779 on OpenAlexaboutno aff
Gilbert Sterling Octavius, Felicia Rusdi Silviani, Alicya Lesmandjaja, Angelina Angelina, Andry Juliansen

Bibliographic record

VenueMiddle East Current Psychiatry · 2020
Typereview
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthAnxietyPsychiatryCoronavirus disease 2019 (COVID-19)MedicinePopulationDepression (economics)Psychological interventionClinical psychologyPsychologyInclusion (mineral)DiseaseEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background The impact of COVID-19 towards psychology and mental health is anticipated to be significant and may affect the population disproportionately, especially adolescent as the vulnerable category. We aimed to analyze the impact of COVID-19 towards adolescents’ mental health. Main body A systematic search was conducted from Cochrane, Google Scholar, Scielo, and PubMed. Inclusion criteria included all types of studies which observed the effect of COVID-19 and its related causes, such as lockdown, on adolescents’ mental health. All studies were assessed for its level of evidence according to Oxford 2011 criteria and Newcastle Ottawa Scale (NOS). Three studies (Seçer and Ulaş, Int J Ment Health Addict: 1–14, 2020; Zhou et al., Eur Child Adolesc Psychiatry 29:749–58, 2020; Qu et al., Lancet: 1–17, 2020) showed that COVID-19 was a risk factor for mental health problems in adolescents while Oosterhoff et al. (J Adolesc Health 67: 179–185, 2020) showed that adolescents who preferred to stay at home during this pandemic reported less anxiety and depressive symptoms Conclusion COVID-19 has been found to be associated with mental health changes in adolescents which meant management of COVID-19 should also focus on mental health as well.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.406
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.210
GPT teacher head0.490
Teacher spread0.280 · 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 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

Citations116
Published2020
Admission routes1
Has abstractyes

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