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Record W2804822880 · doi:10.1093/pch/pxy054.061

PERSPECTIVES ON NEONATAL RESUSCITATION TRAINING IN A CANADIAN PEDIATRIC RESIDENCY PROGRAM: COMMUNITY VERSUS TERTIARY CARE EXPERIENCES

2018· article· en· W2804822880 on OpenAlexaffabout
Mary Woodward, Andrea Hunter, Meghan McConnell, Connie Williams

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNeonatal resuscitationMedical educationCurriculumMedicineNursingPsychologyFocus groupResuscitationPedagogyEmergency medicine

Abstract

fetched live from OpenAlex

Abstract BACKGROUND The competencies involved in neonatal resuscitation include a thorough knowledge of transitional neonatal physiology as well as technical expertise, the ability to lead a multidisciplinary team, and appropriate management of resources. In Canadian paediatric training programs, residents acquire neonatal resuscitation competency in both community and tertiary care settings. There is limited literature regarding experiences of training in variable settings and no literature with respect to the integration of neonatal competency acquisition across training environments. OBJECTIVES To explore residents’ and recent paediatric graduates’ perspectives on acquisition of competencies and neonatal resuscitation training in community and tertiary care centers. DESIGN/METHODS This project employed an interpretive design qualitative methodology, using an a priori educational theory incorporating the principles of social cognitive theory, deliberate practice, distributive practice, and ‘choke phenomenon’. Semi structured focus groups of residents and paediatricians were used for data collection. Interpretive analysis in the style of Crabtree and Miller was employed. Data validity was optimized through member checking and triangulation of themes across investigators. Validity criteria as described by Lincoln and Guba were applied. Institutional ethics board approval was obtained. RESULTS Overall, the participants described a large ‘disconnect’ (lack of communication and congruence of curriculum) between community and tertiary training environments for neonatal resuscitation. Inherent challenges in the community included the variable skill and experience of the interdisciplinary team, availability of resources, and a lack of confidence in their own leadership. In addition, gaps in preceptor knowledge and communication were identified. Strengths of the community setting included: more autonomy for the learner, a high volume of clinical cases with particular emphasis on the ‘normal’; and opportunity for observed feedback with preceptors. In comparison, tertiary center experiences were perceived to be ‘overwhelming’ with a demanding workload and limited opportunity for direct observation and feedback from faculty. Strengths of the tertiary center experience included: variety and high volume of acute clinical cases, facilitating technical skill expertise and self-confidence; and a strong academic focus on physiology and knowledge translation. CONCLUSION Participants described both valuable opportunities and challenges for training and competency acquisition in neonatal resuscitation in tertiary and community settings. Integration of curricula or competencies across settings and across residency level of experience was lacking. This work suggests areas for collaboration within and across training centres to align opportunities in neonatal resuscitation competency 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)0.003
metaresearch head score (Gemma)0.005
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 score0.847
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0190.006
Scholarly communication0.0030.001
Open science0.0020.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.078
GPT teacher head0.399
Teacher spread0.320 · 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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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