Troublesome Knowledge: A New Approach to Quality Assurance in Mental Health Nursing Education
Bibliographic record
Abstract
Background Quality assurance and quality enhancement processes in nursing education are vital to the establishment of a strong program. Existing quality assurance methods in nursing education such as professional self-regulation and external examination rely on provincial and national nursing associations for evaluation, putting minimal responsibility and accountability on internal program examiners. Threshold concepts and troublesome knowledge provide a framework as outlined by Land that utilizes internal examiners from both student and faculty groups and represents an alternative to traditional quality assurance in nursing education. Purpose To identify troublesome mental health nursing content in a nursing curriculum by exploring students and faculty perspectives. Method A sequential mixed methods design that utilized surveys and focus groups to explore student and faculty perspectives on troublesome mental health nursing content. Results The project data were able to be organized into five main content themes that were identified as being troublesome: the spectrum of mental illness, therapeutic relationships and boundaries, praxis, professionalism in nursing, and brain chemistry and its management. Conclusion The findings from this project are unique to the program of review but show the potential of this new approach to quality assurance and program enhancement initiatives in nursing education.
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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 | 0.031 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".