Are educational or quality improvement interventions delivered at the induction or orientation of junior doctors effective?
Bibliographic record
Abstract
There has been significant media scrutiny in the UK of the period when doctors change over into new jobs, with a number of reports highlighting increased mortality. Starting work in a new hospital confers a potential patient safety risk and induction programmes are therefore designed to familiarise doctors with local policies. Little is known about using this time as an opportunity to improve patient outcomes or change practice. The aim was to review interventions which may aid hospital trusts during induction and a strategy to direct future educational and implementation research. A review of Medline, Embase, Cochrane, Scopus and ERIC databases with key terms (induction or orientation, junior doctor or intern, intervention or education or implementation, quality improvement or patient safety or outcome) extracted relevant abstracts. Articles of relevance were analysed and coded as to the type of patient or doctor group, intervention and outcome. Only seven studies were found which generally reported perceived benefits rather than objective outcomes. A significant opportunity to improve evidence based practice and patient safety is being missed by not thoroughly evaluating the impact of induction and orientation of health care professionals.
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 | 0.040 | 0.237 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".