<p>Development of a Clinical Pathway for the Assessment and Management of Suicidality on a Pediatric Psychiatric Inpatient Unit</p>
Bibliographic record
Abstract
PURPOSE: This article describes steps taken by a mental health inpatient multidisciplinary team to develop a clinical pathway for the assessment and management of suicidality in a pediatric psychiatric inpatient unit. PATIENTS AND METHODS: The setting for this project is a 19-bed inpatient psychiatry unit providing care for children and adolescents (6-17 years of age) in a tertiary care pediatric hospital in Ontario, Canada. Three Lean methodologies were used: 1) The A3 process was used to articulate a problem statement and help clarify expectations, determine goals, and uncover, address and encourage discussion of potential issues; 2) Process mapping was used to show how work process activities are sequenced from the time of the patient's admission to discharge; and 3) Standard work, where consideration was given to the breakdown of the work into categories which are sequenced, organized and repeatedly followed. Generally accepted methodologies for developing clinical pathways were used to create a framework and algorithm for the assessment and management of suicidality in psychiatrically hospitalized children and adolescents. RESULTS: The clinical pathway development resulted in six steps from admission to discharge: intake process, inclusion/exclusion criteria, data integration and treatment formulation, interventions, determination of readiness for discharge, and the discharge process. CONCLUSION: This framework, developed with the aim to standardize care for psychiatrically admitted suicidal children and adolescents, may serve as a flexible template for use in similar settings and could be adapted according to local realities and resources.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".