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Record W3138592355 · doi:10.3389/fpsyt.2021.559154

Tutor of Resilience: A Model for Psychosocial Care Following Experiences of Adversity

2021· article· en· W3138592355 on OpenAlexaff
Francesca Giordano, Alessandra Cipolla, Michael Ungar

Bibliographic record

VenueFrontiers in Psychiatry · 2021
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsDalhousie University
FundersBundesministerium für Wirtschaftliche Zusammenarbeit und EntwicklungEuropean Commission
KeywordsPsychological interventionPsychosocialPsychological resilienceMental healthPsychologyIntervention (counseling)Resilience (materials science)Service providerApplied psychologyNursingMedical educationService (business)MedicinePsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

This article describes a model for training service providers to provide interventions that build resilience among individuals who have experienced adversity. The Tutor of Resilience model emphasizes two distinct dimensions to training: (1) transforming service providers' perceptions of intervention beneficiaries by highlighting their strengths and capacity for healing; and (2) flexibly building contextually and culturally specific interventions through a five-phase model of program development and implementation. Tutor of Resilience has been employed successfully with child and youth populations under stress in humanitarian settings where mental health and psychosocial support professionals are required to design and deliver interventions that enhance resilience among vulnerable children.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0030.006
Scholarly communication0.0040.005
Open science0.0030.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.002

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 head0.012
GPT teacher head0.350
Teacher spread0.339 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

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