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Record W4408378962 · doi:10.1136/bmjopen-2024-091342

Access and use of general and mental health services before and during the COVID-19 pandemic: a systematic review and meta-analysis

2025· review· en· W4408378962 on OpenAlexaboutno aff
Camilla Sculco, Beatrice Bano, Eleonora Prina, Federico Tedeschi, Monica Bianca Bartucz, Corrado Barbui, Marianna Purgato, Emiliano Albanese

Bibliographic record

VenueBMJ Open · 2025
Typereview
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMental healthMeta-analysisPsycINFOPandemicPopulationMEDLINEObservational studyCochrane LibraryFamily medicinePsychiatryEnvironmental healthCoronavirus disease 2019 (COVID-19)DiseasePathology

Abstract

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Objectives To quantify access to health services during the COVID-19 pandemic and measure the change in use between the prepandemic and the pandemic periods in a population with assessment of psychological distress or diagnosis of mental disorders. Data sources We developed and piloted a search syntax and adapted it to enter the following databases from 1 January 2020 to 31 March 2023: PubMed/MEDLINE, PsycINFO, Web of Science, Epistemonikos and the WHO International Clinical Trials Registry Platform. We reran the searches from the end of the original search to 3 December 2024. Design We systematically screened titles, abstracts and full texts of retrieved records. Eligibility criteria We included observational studies on any populations and regions, covering health services such as doctor visits, hospital admissions, diagnostic examinations, pharmaceutical therapies and mental health (MH) services. Only studies using validated scales to assess psychological distress or mental disorders as defined in the Diagnostic and Statistical Manual of Mental Disorders were included. Data extraction and synthesis We extracted data using a purposefully designed form and evaluated the studies’ quality with the Newcastle-Ottawa Scale. We measured the incidence rate (IR) of access to health services and the IR ratio (IRR) between the prepandemic and the pandemic periods. We calculated contacts days and catchment areas in the different periods. We used the random effects DerSimonian-Laird inverse-variance model and calculated heterogeneity with statistics I² and τ². We computed pooled IR and pooled IRR and tested the hypothesis of no variation (IRR=1). Results We retrieved 10 014 records and examined the full text of 580 articles. We included 136 primary studies of which 44 were meta-analysed. The IR of access to services during the pandemic was 2.59 contact months per 10 000 inhabitants (IR=2.592; 95% CI: 1.301 to 5.164). We observed a reduction of 28.5% in the use of services with negligible differences by age group and type of services (IRR=0.715; 95% CI: 0.651 to 0.785). We observed significant differences in effect sizes across studies (τ 2 =5.44; p<0.001 and τ 2 =0.090; p<0.001). Conclusion By considering MH, our study provides consolidated evidence and quantifies the reduction in the use of health services during the COVID-19 pandemic. PROSPERO registration number CRD42023403778.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptMeta-epidemiology (broad)
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysishigh
opusMeta-epidemiology (broad)
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysismedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.850
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.401
GPT teacher head0.595
Teacher spread0.194 · 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

Labeled directly by 2 models reading the full record.

Study designMeta-analysis
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
Published2025
Admission routes1
Has abstractyes

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