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Record W4412726793 · doi:10.3390/healthcare13141735

Older People at Risk of Suicide: A Local Study During the COVID-19 Confinement Period

2025· article· en· W4412726793 on OpenAlexaboutno aff
Ismael Puig-Amores, Guadalupe Martín-Mora Parra, Isabel Cuadrado Gordillo, Jessica Morales-Sanhueza

Bibliographic record

VenueHealthcare · 2025
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Coronavirus disease 2019 (COVID-19)Mental healthDemographyObservational studyIncidence (geometry)MedicinePsychological interventionSuicide preventionPublic healthPopulationPandemicGerontologyPoison controlPsychiatryPsychologyEnvironmental healthGeographyDiseaseInfectious disease (medical specialty)NursingSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Suicide in older adults represents an insufficiently addressed public health problem, despite the aging population and the increase in mental disorders in this group. The COVID-19 pandemic and associated measures, such as lockdown, could have exacerbated this phenomenon. This study aimed to analyze the impact of the confinement decreed during the state of alarm in Spain on the incidence of deaths by suicide in people over 70 years of age in Extremadura. METHODS: An observational and retrospective study was carried out, using data from the Institutes of Legal Medicine and Forensic Sciences, comparing the figures for 2020 with the years 2019, 2021, along with the average for the period 2015-2019. Statistical analyses included Chi-square tests and calculation of Relative Risk with 95% CI. RESULTS: The results revealed a significant increase in deaths by suicide in the third quarter of 2020 compared to the periods compared, especially among men. CONCLUSIONS: It is concluded that confinement may have negatively influenced the mental health of older adults, which underscores the need for specific interventions and attention to regional contextual factors.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.384
Teacher spread0.342 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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