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Families as Navigators and Negotiators: Facilitating Culturally and Contextually Specific Expressions of Resilience

2010· article· en· W2115304636 on OpenAlexaffabout
Michael Ungar

Bibliographic record

VenueFamily Process · 2010
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPsychological resilienceNature versus nurturePsychologyMeaning (existential)Mental healthResilience (materials science)Social psychologySociologyPsychotherapist

Abstract

fetched live from OpenAlex

A social ecological model of resilience is used to show that resilience is dependent on a family's ability to both access available resources that sustain individual and collective well-being, as well as participate effectively in the social discourse that defines which resources are culturally and contextually meaningful. In this paper both clinical evidence and a review of the research inform an integrated social ecological model of practice that is focused on advocating for the mental health resources necessary to nurture resilience, including the individual and family processes of coconstruction of meaning. Family therapists can help marginalized families living in challenging contexts develop skills as both navigators who access resources, as well as negotiators who are able to convince therapists and other service providers of what are culturally and contextually meaningful sources of support. A case study of an African-Canadian youth and his family will be presented. The implications of this approach to assessing therapeutic outcomes will also be discussed.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.017
Scholarly communication0.0040.006
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.364
Teacher spread0.347 · 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 designQualitative
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

Citations105
Published2010
Admission routes2
Has abstractyes

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