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Record W3033937661 · doi:10.1177/1066480720929360

The Widowhood Effect: Explaining the Adverse Outcomes After Spousal Loss Using Physiological Stress Theories, Marital Quality, and Attachment

2020· article· en· W3033937661 on OpenAlexaff
Jeffrey Ennis, Umair Majid

Bibliographic record

VenueThe Family Journal · 2020
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsUniversity Health NetworkUniversity of TorontoMcMaster University
Fundersnot available
KeywordsAdverse effectPsychologyQuality of life (healthcare)CLARITYStress (linguistics)Clinical psychologyDevelopmental psychologyGerontologyMedicinePsychotherapist

Abstract

fetched live from OpenAlex

The loss of a loved one is one of the most ubiquitous life experiences. There have been multiple reviews that have found adverse health outcomes for individuals experiencing spousal loss, particularly the widowhood effect that characterizes an increased risk of mortality after loss. However, there is a lack of clarity on the relationship between physiological stress and the widowhood effect. This commentary uses the literature on stress, marital quality, and attachment to explain the widowhood effect and other adverse physical health outcomes. We discuss three points: (1) the chronic nature of stress may be the source of adverse outcomes, (2) the quality and quantity of available resources may moderate the effects of stress, and (3) the level and style of attachment may explain why these outcomes may persist many years after spousal loss.

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)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.072
GPT teacher head0.389
Teacher spread0.317 · 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 designTheoretical or conceptual
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

Citations17
Published2020
Admission routes1
Has abstractyes

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