2018 Ottawa consensus statement: Selection and recruitment to the healthcare professions
Bibliographic record
Abstract
Selection and recruitment into healthcare education and practice is a key area of interest for educators with significant developments in research, policy, and practice in recent years. This updated consensus statement, developed through a multi-stage process, examines future opportunities and challenges in selection and recruitment. There is both a gap in the literature around and a compelling case for further theoretical and empirical literature to underpin the development of overall selection philosophes and policies and their enactment. More consistent evidence has emerged regarding the quality of different selection methods. Approaches to selection are context-dependent, requiring the consideration of an institution's philosophy regarding what they are trying to achieve, the communities it purports to serve, along with the system within which they are used. Diversity and globalization issues continue to be critically important topics. Further research is required to explore differential attainment and explain why there are substantial differences in culturally acceptable ways of approaching diversity and widening access. More sophisticated evaluation approaches using multi-disciplinary theoretical frameworks are required to address the issues. Following a discussion of these areas, 10 recommendations are presented to guide future research and practice and to encourage debate between colleagues across the globe.
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 | 0.204 | 0.324 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.021 | 0.013 |
| Research integrity | 0.042 | 0.034 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".