Barriers and Facilitators to Implementing the HEADS-ED
Bibliographic record
Abstract
OBJECTIVES: This study sought to identify barriers and facilitators to the implementation of the HEADS-ED, a screening tool appropriate for use in the emergency department (ED) that facilitates standardized assessments, discharge planning, charting, and linking pediatric mental health patients to appropriate community resources. METHODS: A qualitative theory-based design was used to identify barriers and facilitators to implementing the HEADS-ED tool. Focus groups were conducted with participants recruited from 6 different ED settings across 2 provinces (Ontario and Nova Scotia). The Theoretical Domains Framework was used as a conceptual framework to guide data collection and to identify themes from focus group discussions. RESULTS: The following themes spanning 12 domains were identified as reflective of participants' beliefs about the barriers and facilitators to implementing the HEADS-ED tool: knowledge, skills, beliefs about capabilities, social professional role and identity, optimism, beliefs about consequences, reinforcement, environmental context and resources, social influences, emotion, behavioral regulation and memory, and attention and decision process. CONCLUSIONS: The HEADS-ED has the potential to address the need for better discharge planning, complete charting, and standardized assessments for the increasing population of pediatric mental health patients who present to EDs. This study has identified potential barriers and facilitators, which should be considered when developing an implementation plan for adopting the HEADS-ED tool into practice within EDs.
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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 | 0.027 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".