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Record W4206473569 · doi:10.22158/eshs.v2n3p1

A Qualitative Course-based Inquiry into the Use of Strengths-based Language in Child and Youth Care Residential Field Practicums

2021· article· en· W4206473569 on OpenAlexaffabout
Gerard Bellefeuille, Lerynne Biton, Yulieth Chinchilla, Francesca Doniego, Hiba Iqbal, Vivian Lin, Anett Parokkaran, Angelo Sison

Bibliographic record

VenueEducation Society and Human Studies · 2021
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsMacEwan University
Fundersnot available
KeywordsCognitive reframingFeelingCornerstonePsychologyQualitative researchQualitative propertyField (mathematics)Product (mathematics)SociologySocial psychologyComputer scienceGeographySocial science

Abstract

fetched live from OpenAlex

The strengths-based approach is a cornerstone of relational-centered Child and Youth Care (CYC) practice. However, few studies have investigated the use of the strengths-based approach in a CYC residential setting for youth and children. Hence, this qualitative course-based study explores the use of strengths-based language as observed by CYC students in residential field practicums. Data were collected through an online semi-structured interview (using the Google Meet platform) with a purposive sample of third- and fourth-year CYC students at MacEwan University, Canada. Four main themes were extracted from the data analysis: “not in plain sight”, “a product of feeling stressed”, “lacking the confidence to speak out”, and “reframing”.

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)0.012
metaresearch head score (Gemma)0.013
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 score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.011
Scholarly communication0.0030.003
Open science0.0030.005
Research integrity0.0020.003
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 head0.172
GPT teacher head0.520
Teacher spread0.348 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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