The paediatric developmental toolkit: Facilitating learning of child development
Bibliographic record
Abstract
BACKGROUND: Recent paediatric and family medicine graduates report feeling unprepared to identify and address children and youth with developmental disorders. Developmental history taking and physical examination alone limit engagement with children and youth in an interactive manner to assess development. The paediatric developmental toolkit (PDT) was developed to provide trainees with the opportunity to interact with a child in a play-based manner. OBJECTIVES: The primary objective of this study was to determine the feasibility of PDT within clinical settings, and qualitatively explore how the PDT can be used by teachers and trainees. METHODS: Trainees and their clinical teachers participated in a qualitative study. Trainees used the PDT in clinical settings and were interviewed following their clinical encounters. Interactions between clinical teachers and trainees following the use of the PDT were also recorded. Teachers were interviewed following the trainees' case presentations and closures of clinic visits. Trainee interviews, teacher and trainee interactions, and teacher interviews were audiotaped, transcribed, and analyzed thematically. RESULTS: Nine trainees (six paediatric residents, two family medicine residents, and one clinical clerk medical student) and four developmental paediatricians participated in the study. Each trainee used the PDT twice in two different clinical encounters. All residents agreed the PDT enabled them to observe a child's developmental skills in a short period of time. Clinical teachers all felt the toolkit allowed trainees to more holistically consider a child's development and diagnosis. CONCLUSIONS: As medical education shifts to a competency-based education curriculum, the PDT is an innovative tool that can be used to enhance paediatric and family medicine residents' learning of child development by enabling opportunities for interaction with children.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".