Are paediatric cardiology textbooks obsolete in the current digital era?
Bibliographic record
Abstract
INTRODUCTION: With the rise of online references, podcasts, webinars, self-test tools, and social media, it is worthwhile to understand whether textbooks continue to provide value in medical education, and to assess the capacity they serve during fellowship training. METHODS: textbook, followed by the completion of the survey. Open-ended questions included theming and grouping responses as appropriate. RESULTS: The survey was completed by 36 participants. When asked about the content, organisation, and utility of the chapter, responses were generally positive, at greater than 89%. The chapters, overall, were rated relatively easy to read, scoring at 6.91, with standard deviations plus or minus 1.72, on a scale from 1 to 10, with higher values meaning better results. When asked to rank their preferences in where they obtain educational content, textbooks were ranked the second highest, with in-person teaching ranking first. Several themes were identified including the limitations of the use of textbook use, their value, and ways to enhance learning from their reading. There was also a near-unanimous desire for more time to self-learn and read during fellowship. CONCLUSIONS: Textbooks are still highly valued by trainees. Many opportunities exist, nonetheless, to improve how they can be organised to deliver information optimally. Future efforts should look towards making them more accessible, and to include more resources for asynchronous learning.
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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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".