Learning and quality improvement : nursing in the pediatric intensive care unit
Bibliographic record
Abstract
Maintaining a high quality of care in a Pediatric Intensive Care Unit (PICU) is a constant challenge. Continual 24/7 staffing, ongoing staff turnover, and the constant introduction of new equipment and procedures in a highly technologically-dependent unit requires continuous learning to deliver and improve the quality of children’s care. While all staff consider continuous learning important to maintaining and improving care, learning as quality improvement is made most explicit when new nursing staff are hired and incorporated into the PICU. In this dissertation, I investigated the process of learning by individuals in the interactive social environment of the PICU to answer the following questions: How does learning occur among the newly hired nurses in the PICU? And, how does learning contribute to quality improvement? In this mixed method inquiry, I employed ethnography, Social Network Analysis and simple descriptive and inferential statistical methods to explore process of learning among the newly hired nurses in Western Canada Hospital. I found that learning among newly hired nurses happened through face to face interactions in the context of two main activities: Orientation sessions and their Preceptorship. The most significant learning for the newly hired nurses, however, happened during their Preceptorship. Learning in the Preceptorship was social and experiential as they moved from legitimate peripheral participation in the multi-disciplinary and complementary social environment of the PICU into full participation as members of the PICU Community of Practice (CoP). This learning required the transformation and development of their individual and collective identity, as their preceptors, fellow nurses, and other staff employed scaffolding to mentor them through their constantly evolving Zone of Proximal Development (ZPD). Social and experiential learning activities became the basis for continuous quality improvement (CQI). I conclude that, in the PICU, quality improvement is the tangible manifestation and product of social and experiential learning. Rather than a sequence of corrective actions, in its most effective form, quality of care is improved through scaffolded ongoing learning activities in the authentic setting of a CoP. I recommend the unit to adopt a “learning together” sociocultural approach with scaffolding as key component for successful learning and CQI.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".