So much to teach, so little time: a prospective cohort study evaluating a tool to select content for a critical care curriculum
Bibliographic record
Abstract
INTRODUCTION: Curricular content is often based on the personal opinions of a small number of individuals. Although convenient, such curricula may not meet the needs of the target learner, the program or the institution. Using an objective method to ensure content validity of a curriculum can alleviate this issue. METHODS: A form was created that listed clinical presentations relevant to residents completing intensive care unit (ICU) rotations. Twenty residents and 20 intensivists in tertiary academic multisystem ICUs ranked each presentation on three separate scales: how life-threatening each is, how commonly each is seen in critical care, and how reversible each is. Mean scores for the individual scales were calculated, and these three values were subsequently multiplied together to achieve a composite score for each presentation. The correlation between the two groups' scores for the presentations was calculated to assess reliability of the process. RESULTS: There was excellent agreement between the two groups for rating each presentation (correlation coefficient r = 0.94). The 10 clinical presentations with the highest composite scores formed the basis of our new curriculum. CONCLUSIONS: We describe a method that can be used to select the content of a curriculum for learners in an ICU. Although the content that we selected to include in our curriculum may not be applicable to other ICUs, we believe that the process we used is easily applied elsewhere, and that it provides an efficient method to improve content validity of a curriculum.
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 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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".