94: An Organizational Ethics Model of Roles, Challenges, and Quality Indicators of End-of-Life Care in the Neonatal Intensive Care Unit (NICU)
Bibliographic record
Abstract
Quality of care during decision-making and at end-of-life influences how parents cope with neonatal death. Associated moral distress affects individuals and an organization's ethical climate. To explore the challenges of meeting parents' and infants' needs in end-of-life care and decision-making in the NICU. Thirty-six semi-structured interviews were undertaken with 43 health care workers (HCW) from one tertiary Canadian NICU: 23 nurses, five neonatologists, five residents, three social workers, two fellows, two nurse practitioners, two chaplains, and one dietician. Questions (developed by a multidisciplinary research team including parents) probed for HCW's experiences, perception of parent needs, and challenges to providing quality end-of-life care. Recorded interviews were transcribed, thematic analysis performed with triangulation of themes across investigators, and an organizational ethics model of end-of-life care evolved. Figure 1 depicts an organizational model of roles, challenges, and quality indicators of end-of-life care based on HCW themes. The ability to meet parents' and infants' needs were influenced by intrinsic and extrinsic factors related to HCW's roles and perception of team function. These were influenced by prior experiences and training, personal and societal values, and situational context. Similarly, indicators of quality care were both intrinsic (self-satisfaction) and extrinsic (parent feedback). Consequences of poor quality of care resulted in ‘harm’ to both families and HCWs. Finally, lack of institutional supports and resources were barriers to care (Figure 1). An organizational model identifying HCW roles and challenges in providing quality end-of-life care was developed through stakeholder interviews. This model may guide institutional identification of quality indicators and inform the development of policies and programs aimed toward improving end-of life care practices.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".