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Record W2610203822 · doi:10.1111/jcap.12166

Hospital rules and regulations: The perspectives of youth receiving psychiatric care

2017· article· en· W2610203822 on OpenAlexaff
C Gros, Ciara Parr, David Wright, Marjorie Montreuil, Julie Fréchette

Bibliographic record

VenueJournal of Child and Adolescent Psychiatric Nursing · 2017
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of OttawaRegistered Nurses' Association of OntarioUniversité du QuébecMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsPerceptionGeneral partnershipPerspective (graphical)MoodUnintended consequencesPsychologyCommon senseQualitative researchPsychiatryMedicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Rules and regulations represent an aspect of psychiatric hospitalization about which little is known. STUDY PURPOSE: To explore the perceptions of rules from the perspective of youth receiving hospital-based psychiatric services. DESIGN: Qualitative descriptive. METHODS: Perceptions of rules were elicited through semi-structured interviews with a convenience sample of six youth. RESULTS: Rules were perceived as governing virtually all aspects of everyday living in the hospital environment. Rules were used to structure daily activities, routines, and social interactions, and were embedded within clinical protocols and treatment plans. For each participant, "making sense" or "not making sense" were central themes through which rules were interpreted as being either therapeutic or oppressive. Rules that made "no sense" negatively affected youth mood, behavior, treatment adherence, and engagement in a collaborative relationship. CONCLUSION: Working in partnership with youth in psychiatric care to establish, implement, and evaluate rules that "make sense" can promote positive health outcomes and prevent negative, unintended consequences.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.366
Teacher spread0.341 · 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 teacher head, not a consensus.

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

Citations11
Published2017
Admission routes1
Has abstractyes

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