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Record W4226241727 · doi:10.1186/s12909-022-03306-w

The perceptions and experiences of medical students in a pediatric buddy program: a qualitative study

2022· article· en· W4226241727 on OpenAlexaff
Candace Nayman, Jeffrey Do, Alexa Goodbaum, Kaylee Eady, Katherine Moreau

Bibliographic record

VenueBMC Medical Education · 2022
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGeneral partnershipThematic analysisEmpathyMedical educationQualitative researchPerceptionPediatric oncologyMedicinePsychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Partnership programs between medical students and patients provide students with non-clinical experiences that enhance medical learning, especially with respect to humanistic care. We explored the perceptions and experiences of medical students in a pediatric oncology buddy program. METHODS: Using a basic interpretive qualitative approach, we conducted interviews with 15 medical students at three time points: before meeting his/her buddy (pre-interview), four months into the partnership (4-month interview), and at the end of the partnership (post interview). We then conducted a thematic analysis of the interview data. RESULTS: All students in the program who met the study criteria (N = 15/16) participated. The medical students highlighted that: (a) providing support to buddies and their families is important; (b) providing care to children with serious illnesses is emotionally difficult; (c) developing deep connections with buddies and their families is rewarding; and (d) gaining empathy and personal fulfillment from buddies and their families is inevitable. CONCLUSIONS: This study provides an understanding of medical students' perceptions and experiences in a pediatric oncology, non-clinical buddy program. Tailored one-on-one partnerships between medical students and pediatric oncology patients play an important role in medical education and contributes to the teaching of humanistic care.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.486
Teacher spread0.446 · 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 teacher head, not a consensus.

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

Citations7
Published2022
Admission routes1
Has abstractyes

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