Using web-based training to optimize pediatric palliative care knowledge transfer
Bibliographic record
Abstract
Nurses play a central role in delivering palliative care, given their influence on the quality of care provided (Montgomery, Sawin, & Hendricks-Ferguson, 2016). They are professionals of choice when it comes to assessing disease symptoms or psychological distress, ensuring symptoms are managed effectively, as well as accompanying patients and families through the decision-making process regarding both adult and pediatric care (Contro, Larson, Scofield, Sourkes, & Cohen, 2004). Optimal palliative care practices can prevent or alleviate the suffering of patients of all ages at the end of life, particularly if the care includes the assessment of symptoms and provides the patient and his or her family with psychological and social support (Qaseem et al., 2008). Although the majority of patients receiving palliative care are adults, more than 4,000 children in Canada have an incurable disease for which they will require quality palliative care (Widger, Cadell, Davies, Siden, & Steele, 2012). However, a number of studies carried out with nurses have revealed that they experience anxiety with regard to the pediatric palliative care (PPC) they deliver (Mullen, Reynolds, & Larson, 2015) and difficulties communicating with families of patients (Montgomery et al., 2017), as well as managing their emotions when they attend to a child who is at the end of life (Roberts & Boyle, 2005). Based on the first hypothesis suggested by Contro et al. (2004), such behaviour can be explained by a lack of knowledge regarding PPC.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".