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Record W4413478774 · doi:10.1016/j.acap.2025.103131

Discovering What Works Well: Exploring Primary Palliative Care Education in Pediatrics Residency Programs in Canada

2025· article· en· W4413478774 on OpenAlexafffundabout
Naomi Goloff, Lea Sultanem, Alexis Fong-Leboeuf, Estee Grant, Elizabeth Anne Kinsella, Robin Williams, Daniëlle Verstegen, Erin Kwolek

Bibliographic record

VenueAcademic Pediatrics · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAppreciative Inquiry and Organizational Change
Canadian institutionsWestern UniversityMcMaster UniversityUniversity of OttawaMcGill UniversityUniversity of CalgaryMcGill University Health Centre
FundersFaculty of Medicine, McGill UniversityMcGill University
KeywordsPrimary careMedicinePalliative careFamily medicinePediatricsMedical educationNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score0.831

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0190.007
Scholarly communication0.0080.002
Open science0.0020.006
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.044
GPT teacher head0.259
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes3
Has abstractno

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