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Record W2101295299 · doi:10.1002/rnj.204

Resilience in Families With Adolescents Suffering From Traumatic Brain Injuries

2015· article· en· W2101295299 on OpenAlexaff
Jérôme Gauvin‐Lepage, Hélène Lefebvre, Denise Malo

Bibliographic record

VenueRehabilitation Nursing · 2015
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsIntervention (counseling)RehabilitationPsychological resilienceResilience (materials science)PsychologyProcess (computing)Data collectionApplied psychologyNursingFamily resilienceMedical educationMedicinePsychotherapistPsychiatryComputer scienceSociology

Abstract

fetched live from OpenAlex

PURPOSE: This study aims to coconstruct the building blocks for an intervention program to support family resilience in conjunction with families with an adolescent suffering from traumatic brain injury and rehabilitation professionals. DESIGN: This is a qualitative and inductive study, supported by a collaborative research approach. METHODS: Based on the complex intervention design and validation model, the investigator follows a three-stage data collection process: (1) identifying the building blocks of the intervention program in the eyes of families and rehabilitation professionals, (2) prioritizing, and (3) validating the building blocks with the same participants. FINDINGS: After analyzing the data, the investigator identifies five encompassing themes as the building blocks of the intervention program. CONCLUSIONS/CLINICAL RELEVANCE: This study offers promising avenues for practitioners and researchers in nursing and other fields with respect to the implementation of concrete strategies to support the resilience process of families facing particularly difficult times in their lives.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.002
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.387
Teacher spread0.359 · 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 designObservational
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

Citations16
Published2015
Admission routes1
Has abstractyes

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