The Multiple Mini Interview for admission to nursing – male perspectives
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Aims: The aim of this study was to gain the perspectives of men undergoing recruitment to a nursing degree programme by the process of multiple mini interviews (MMIs). Background: MMIs are used increasingly to select undergraduate students for degree courses, particularly in the healthcare sciences but the impact of MMIs on initiatives to increase gender diversity in these professions is unknown. Design: The study employed a qualitative research approach using a thematic framework of the MMI process. Methods: The study took place between January 2018 - April 2018 and a total of eight students attended for focus groups. Results: Respondents viewed the MMI process as stressful, and also reported that some of the stations created more stress than others, as they were conscious of the gender issues within some of the scenarios. Despite this they also reported the MMI to be a satisfactory selection tool. Conclusion: Participants found the use of MMIs to comprise a valid selection process which, while imperfect and female-dominated, did not unduly disadvantage male candidates. Further research involving multiple nursing schools as well as medical schools is needed to further evaluate the impact of the MMI as a selection tool on male applicants.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".