Systematic Approach to Remediation in Basic Science Knowledge for Preclinical Students: A case study
Bibliographic record
Abstract
Remediation of pre-clerkship students for deficits in basic science knowledge should help them overcome their learning deficiencies prior to clerkship. However, very little is known about remediation in basic science knowledge during pre-clerkship. This study utilized the program theory framework to collect and organize mixed methods data of the remediation plan for pre-clerkship students who failed their basic science cognitive examinations in a Canadian medical school. This plan was analyzed using a logic model narrative approach and compared to literature on the learning theories. The analysis showed a remediation plan that was strong on governance and verification of scores, but lacked: clarity and transparency of communication, qualified remedial tutors, individualized diagnosis of learner’s deficits, and student centered learning. Participants admitted uncertainty about the efficacy of the remediation process. A remediation framework is proposed that includes student-centered participation, individualized learning plan and activities, deliberate practice, feedback, reflection, and rigorous reassessment
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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".