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Family Resilience

2016· other· en· W4231104365 on OpenAlexaff
James P. Coyle

Bibliographic record

VenueEncyclopedia of Family Studies · 2016
Typeother
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNormativePsychological interventionPsychological resilienceFamily resiliencePsychologyProtective factorResilience (materials science)MedicineSocial psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Abstract Family resilience is a dynamic process in which families successfully cope with or adapt to adverse events or conditions that place them at risk for poor outcomes. This occurs when families use family, individual, and community resources that compensate for or assist in the recovery from adverse events to achieve positive outcomes. Research has examined the impact of common risk factors, for example, physical or mental illness, disability, substance abuse, normative transitions, and disasters, and the benefits from protective factors such as positive family functioning, spiritual beliefs, or supportive resources outside the family. Family resilience‐based treatment approaches assess interactions between risk factors and available protective factors and focus on interventions that enhance protective 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 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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.063
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0630.004

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 head0.045
GPT teacher head0.415
Teacher spread0.370 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2016
Admission routes1
Has abstractyes

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