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Record W4391754215 · doi:10.3390/youth4010017

Quality Care in Residential Care and Treatment Settings in North America: From Complex Research to Four Everyday Principles for Practice

2024· article· en· W4391754215 on OpenAlexaff
Kiaras Gharabaghi

Bibliographic record

VenueYouth · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsQuality (philosophy)Residential careNursingEnvironmental planningPsychologyMedicineBusinessGeographyEpistemology

Abstract

fetched live from OpenAlex

Quality is a central topic in contemporary discussions about residential care, and specifically about group or congregate care. Such care settings have been contested in recent years specifically resulting from anecdotal evidence that quality is lacking. To this end, the response has focused on the development of quality indicators and standards. In this essay, the author argues that, although such approaches are necessary and have helped to embed evidence-based practices in residential care settings, they are not easily translated into everyday practice. Quality care must mean more than frameworks for care that are governed by professional system designs. Quality care also must include the experiences of young people living life in these settings. To this end, to help with the translation of quality care standards for residential care, the essay presents four core principles that, on the one hand, are familiar and easily translatable for youth workers and social workers in these settings, and on the other hand, honour and are congruent with core elements of almost all evidence-based practice approaches.

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)0.046
metaresearch head score (Gemma)0.042
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: none
Teacher disagreement score0.058
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0200.087
Scholarly communication0.0240.020
Open science0.0040.018
Research integrity0.0070.014
Insufficient payload (model declined to judge)0.0010.000

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 head0.276
GPT teacher head0.518
Teacher spread0.242 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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