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Record W3210830816 · doi:10.1093/pch/pxab061.120

152 Transition to residency - Evaluation of a novel bootcamp for incoming paediatric residents

2021· article· en· W3210830816 on OpenAlexaff
Kathryn Hynes, Mia Remington

Bibliographic record

VenuePaediatrics & Child Health · 2021
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsCurriculumMedicineContext (archaeology)Medical educationLikert scaleExperiential learningCoronavirus disease 2019 (COVID-19)PandemicFamily medicinePsychologyPedagogy

Abstract

fetched live from OpenAlex

Abstract Primary Subject area Medical Education Background In 2020, medical students experienced a sudden change in their learning context due to the COVID-19 pandemic. University policies and public health recommendations removed medical students from their clinical learning environments. Given this shift from work-based learning, incoming residents and educators alike were wary of the impact on residency readiness. With the current context in mind, and with an approaching CBD launch, the UBC Pediatrics training program developed a bootcamp curriculum in an attempt to ease residency transition. This month-long rotation included instruction and experiential learning in all CanMED roles, with heavy focus on medical expert, communication and collaboration skills required of new residents. Wellness topics were also included given the additional stressors associated with the pandemic. Objectives 1). Develop a novel bootcamp curriculum for incoming pediatrics residents with a focus on all CanMED roles in the context of the unique learning constraints during the COVID-19 pandemic. 2). Use cross-sectional survey data to assess comfort levels of new pediatrics residents in all CanMED roles prior to, and after, participating in bootcamp. 3). Discuss implications for medical educators in transitioning new residents into their role, given current limitations imposed by the pandemic. Design/Methods We created and distributed a cross-sectional survey to 19 incoming pediatric residents through a secure online platform (RedCAP). Using a 5-point likert scale, survey questions focused on assessing resident comfort levels with competencies from all CanMED roles prior to, and after, participating in bootcamp. Free text comments were included to expand on quantitative data. Results Response rate was 100% (19/19) for pre-bootcamp surveys, and 84% (16/19) for post-bootcamp surveys. Prior to residency, respondents reported feeling uncomfortable with physical exam skills, pediatric hospital medicine and procedures. After the bootcamp block, participants noted increased comfort with medical management in acute care settings (for example, approach to pediatric cardiac arrest), procedural skills such as LPs, and managing common on call problems (for example, electrolyte disturbances and antibiotics). In free text comments, simulation training was highlighted as being particularly important for building communication and teamwork skills. Trainees seemed to find enjoyment in learning new wellness techniques such as mindfulness and narrative medicine. Residents also noted increased social connection within the group. Conclusion Our results suggest that during a period of heightened vulnerability, purposeful attention to transitioning new residents into their new role through a bootcamp rotation provided increased comfort and confidence in many CanMED roles. Furthermore, residents endorsed increased cohesion and acknowledged the social benefits of participating in team-based learning, a particularly important and potentially protective outcome given current limitations imposed by the pandemic.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.387
Teacher spread0.342 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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