A need for change: A reflective account of the introduction of photo elicitation to the selection process for students of nursing and midwifery
Bibliographic record
Abstract
Attrition from programmes of health and social care education is a current cause for concern within all higher education environments. Not only are institutional finances burdened by attrition, but also its impact on the remaining students, commissioning services and ultimately the professions’ themselves are well noted. A number of key areas have been highlighted as contributing to the reduction of attrition, amongst them selection and recruitment is seen as key. Using a reflective approach current strategies used within a Higher Education (HE) facility in the east of England are considered. Through analysis and evaluation of a newly introduced strategy, that of using photo elicitation, its valuable contribution is asserted. Traditionally, candidates have been selected and recruited using a variety of screening tools, not least the face to face interview. It is conceivable that, if this approach is to be able to confidently reduce attrition and retain quality students investment in the process is essential. Herein, within this paper the use of photo elicitation is examined. Considered in the context of usage as an interview technique, it’s worth in the recruitment process of students to health care courses is evaluated. A meaningfulness is asserted in identifying candidates whose values and beliefs are aligned with both the HE establishment and the commissioning bodies within the health care setting portraying them as ‘best match’ to the requisite profile.
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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 | 0.097 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.041 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.011 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 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".