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Record W4413639375 · doi:10.1007/s44250-025-00285-9

Male caregivers: a scoping review of impacts, needs, challenges, and best practices

2025· review· en· W4413639375 on OpenAlexaff
Bertine Sandra Akouamba, Sophie Audette-Chapdelaine, Ionela Gheorghiu, Hinatea Lai, Maggy Wassef, A. J. Mares, Walter S. Marcantoni

Bibliographic record

VenueDiscover Health Systems · 2025
Typereview
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsBest practicePsychologyBusinessPolitical science

Abstract

fetched live from OpenAlex

Although they play a crucial role in care trajectories and the quality of life of those they support, male caregivers (MCs) remain largely unrecognized, and the services available to assist them remain often insufficient or inadequate. This scoping review aims to identify their needs, challenges, and practices to foster an optimal support framework. Five scientific databases and the grey literature were reviewed for publications produced worldwide since 2000. Ninety-nine studies were included and the results showed that MCs encounter emotional, physical, social, economic, and legal challenges, necessitating flexible and tailored approaches. The findings also highlight the crucial role of family, community, and healthcare system support, as well as the importance of access to personalized support services and training programs that address their specific needs. This scoping review may contribute to the development and implementation of more inclusive and better-adapted interventions, services and programs that are better suited to the realities of MCs.

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)0.019
metaresearch head score (Gemma)0.060
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 score0.020
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.060
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0200.019
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.124
GPT teacher head0.454
Teacher spread0.330 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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