Evaluating the Influence of Technology-Enhanced Teaching Methods on the Development of Clinical Reasoning Skills in Medical Education: A Qualitative Systematic Review
Bibliographic record
Abstract
Search strategyFollowing a PRISMA model, a Systematic search of six major databases will be conducted, including MEDLINE, EMBASE, CINAHL, ERIC, PsychINFO, and Scopus.I will focus on three primary search terms: "Medical e d u c a t i o n " A N D " c l i n i c a l re a s o n i n g , " i n combination with " artificial intelligence" or "Augmented reality" or "Virtual Reality" or "serious Game" or "Simulation" or "Virtual Patient Online Learning" or "Mobile Applications" or "Wearable Technology" or "Telemedicine / Telehealth".Both keywords and MeSH terms will be utilized. Participant or populationThe eligible study participants span all levels of medical trainees and practitioners, including medical students, interns, residents, fellows and attending physicians.Intervention Intervention includes experimental use of technology-enhanced teaching methods.Thus, studies that examined pedagogical intervention that did not utilize technology to enhance teaching were excluded. Comparator Traditional teaching methods.Study designs to be included All experimental and empirical studies will be included in this review. Eligibility criteriaIn terms of time, starting from 1975 because it was the first published article, to the year 2023.
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 | 0.071 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".