MétaCan
Menu
Back to cohort
Record W2884047458 · doi:10.1177/1054773818791085

Immediate Staff Debriefing Following Seclusion or Restraint Use in Inpatient Mental Health Settings: A Scoping Review

2018· review· en· W2884047458 on OpenAlexafffund
Remar A. Mangaoil, Kristin Cleverley, Elizabeth Peter

Bibliographic record

VenueClinical Nursing Research · 2018
Typereview
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCentre for Addiction and Mental Health
KeywordsDebriefingSeclusionMental healthDocumentationPsychologyMedicineNursingMedical educationPsychiatry

Abstract

fetched live from OpenAlex

The aim of this scoping review is to synthesize the academic and gray literature on the use of immediate staff debriefing following seclusion or restraint events in inpatient mental health settings. Multiple electronic databases were searched to identify literature on the topic of immediate staff debriefing. The analysis identified several core components of immediate staff debriefing: terminology, type, critical reflection, iterative process, training, documentation, and monitoring. While these components were regarded as vital to the implementation of debriefing, they remain inconsistently described in the literature. Immediate staff debriefing is an important intervention not only to prevent future episodes of seclusion and restraint use, but as a forum for staff to support each other emotionally and psychologically after a potentially distressing event. The core components identified in this review should be incorporated into the organization's policies, practice guidelines, and training modules to ensure consistent conceptualization and implementation of the debriefing process.

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)0.014
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.074
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0130.012
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.502
GPT teacher head0.673
Teacher spread0.171 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations36
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueClinical Nursing ResearchSame topicHealthcare Decision-Making and RestraintsFrench-language works237,207