Discovering What Works Well: Exploring Primary Palliative Care Education in Pediatrics Residency Programs in Canada
Bibliographic record
Abstract
Background Pediatricians require primary palliative care (PC) skills – communication, pain and symptom management, and psychosocial support – to provide care that mitigates suffering for children with serious illnesses. Residents may not develop skills adequately, and little is known about how they learn those that they do have. Objective To explore effective primary PC learning in Canadian pediatrics residency programs. Methods Using Appreciative Inquiry methodology, we focused on ‘what is working well' to explore resident learning. We purposively sampled 17 trainees (post-graduate years 3-5), representing 13/17 programs. Participants engaged in semi-structured interviews, which we transcribed and analyzed iteratively through an inductive thematic process. Results The findings highlighted two predominant themes: a) Embracing incidental learning in the workplace, and b) Scaffolding learning through balanced structure and autonomy. Subthemes included: Recognizing the value of informal and unexpected learning opportunities; Strategies for harnessing incidental learning; Fostering interprofessional collaboration for learning; Integrating PC throughout training; Balancing structured learning with workplace-based opportunities for skill development; and the importance of graduated responsibility in workplace learning. Conclusions The residency learning environment provides a rich milieu to develop primary PC skills, but it is often difficult to make use of the fragmented learning opportunities. Residents rely significantly on unplanned clinical opportunities and must actively engage in planning, monitoring, and reflecting on their experiences to develop these skills. Our study underscores the importance of a multi-faceted approach to acquisition of PC skills – through experiential learning, reflective practice, graded responsibility, mentorship opportunities – spread throughout the duration of training.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".