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Record W4401229917 · doi:10.1111/jcap.12477

Pediatric psychiatric inpatients' perspectives of aggression management: Discernment in the doorway

2024· article· en· W4401229917 on OpenAlexaff
Melissa Adrian, Graham McCaffrey

Bibliographic record

VenueJournal of Child and Adolescent Psychiatric Nursing · 2024
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSeclusionDiscernmentFeelingPsychological interventionAggressionPsychiatric hospitalPsychiatryMedicinePerspective (graphical)HarmMeaning (existential)PsychologyNursingPsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

PROBLEM: Aggressive behavior is common on psychiatric inpatient units. Seclusion and restraint interventions to manage patients' aggressive behavior may have the consequence of being traumatizing for patients. Pediatric psychiatric patients' perspective on the use of seclusion and restraint interventions is not present in the literature. METHODS: This hermeneutic nursing research study asked the question, "How might we understand children's experiences of seclusion and restraints on an inpatient psychiatric unit?" Four past pediatric psychiatric inpatients shared their hospitalization experiences that occurred within the previous year when they were 10 years old. The texts of the research interviews were compared to Attachment Theory for a deeper understanding of the meaning of the message. FINDINGS: Participants commonly described experiences with seclusion and restraints as feeling trapped and alone in a dark room. They recommended the nurses step into the room with them to help them heal. Interpretively, the rooms on inpatient units could be considered as actual and metaphorical spaces of possible harm or healing. CONCLUSION: The participant's voices expand understanding of nurse's use of discernment at the doorway of a patient room to ensure the most therapeutic care is provided to the patient in these spaces through a secure nurse-patient relationship.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.010
GPT teacher head0.333
Teacher spread0.322 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations1
Published2024
Admission routes1
Has abstractyes

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