Barriers and Enablers to Implementing a High-Dependency Care Model in Pediatric Care
Bibliographic record
Abstract
BACKGROUND: As the level of acuity of pediatric hospital admissions continues to increase, additional pressure is being placed on hospital resources and the nursing workforce. LOCAL PROBLEM: Currently, there is no formalized approach to care for high-acuity patients on our pediatric inpatient unit. METHODS: We used a qualitative descriptive design, guided by the Theoretical Domains Framework and Capability, Opportunity, Motivation-Behaviour (COM-B) model, to conduct focus groups and interviews with clinicians and administrators to identify potential barriers and enablers to implementing a high-dependency care (HDC) model. An HDC model focuses on the relationship between adequate nursing staff resources and patient acuity to improve patient health outcomes. RESULTS: Participants identified the need for clear guidelines and supportive physical structures to facilitate HDC implementation. Anticipated benefits included enhanced nursing confidence and family-centered care. CONCLUSIONS: Study findings highlight multilevel factors to consider prior to implementing an HDC model on a pediatric inpatient unit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".