Quality assurance and quality enhancement of the nursing curriculum – happy marriage or recipe for divorce?
Bibliographic record
Abstract
Background: This study investigated nurse academics’ perceptions of a curriculum review process that married a ‘top down’ quality assurance process with a ‘bottom up’ quality enhancement process in a Bachelor of Nursing curriculum. Methods: Focus group interviews were held with seven nurse academics on two campuses of a regional Australian university. The data were analyzed thematically. Results: Overall nurse academics found value in both the collegial sharing of ideas in the ‘bottom up’ unit review meetings and in the ‘top down’ unit reporting process. However their perceptions of the curriculum review process highlighted a number of tensions that clustered around the following main themes: clarity of communication, sensitivity/validity of review data, the impact of contextual factors, and the risk of ritualized practice. Conclusions: These results highlight that quality assurance and quality enhancement processes can be married, but clear communication is needed about the purposes of the curriculum review process and its constraints, and a mechanism is required to close the quality feedback loop. Nursing academics need to take ownership of the process to find ways to work around contextual constraints that impact on the success of the curriculum review process.
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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 | 0.044 | 0.103 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".