Engaging Pediatric Intensive Care Unit (PICU) clinical staff to lead practice improvement: the PICU Participatory Action Research Project (PICU-PAR)
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Despite considerable efforts, engaging staff to lead quality improvement activities in practice settings is a persistent challenge. At British Columbia Children's Hospital (BCCH), the pediatric intensive care unit (PICU) undertook a new phase of quality improvement actions based on the Community of Practice (CoP) model with Participatory Action Research (PAR). This approach aims to mobilize the PICU 'community' as a whole with a focus on practice; namely, to create a 'community of practice' to support reflection, learning, and innovation in everyday work. METHODOLOGY: An iterative two-stage PAR process using mixed methods has been developed among the PICU CoP to describe the environment (stage 1) and implement specific interventions (stage 2). Stage 1 is ethnographic description of the unit's care practice. Surveys, interviews, focus groups, and direct observations describe the clinical staff's experiences and perspectives around bedside care and quality endeavors in the PICU. Contrasts and comparisons across participants, time and activities help understanding the PICU culture and experience. Stage 2 is a succession of PAR spirals, using results from phase 1 to set up specific interventions aimed at building the staff's capability to conduct QI projects while acquiring appropriate technical skills and leadership capacity (primary outcome). Team communication, information, and interaction will be enhanced through a knowledge exchange (KE) and a wireless network of iPADs. RELEVANCE: Lack of leadership at the staff level in order to improve daily practice is a recognized challenge that faces many hospitals. We believe that the PAR approach within a highly motivated CoP is a sound method to create the social dynamic and cultural context within which clinical teams can grow, reflect, innovate and feel proud to better serve patients.
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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 | 0.051 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it