Striving for best practice: standardising <scp>N</scp>ew <scp>Z</scp>ealand nursing procedures, 1930–1960
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To identify how nurses in the past determined best practice, using the context of New Zealand, 1930-1960. BACKGROUND: In the current context of evidence-based practice, nurses strive to provide the best care, based on clinical research. We cannot assume that nurses in the past, prior to the evidence-based practice movement, did not also have a deliberate process for pursuing best practice. Discovering historical approaches to determining best practice will enrich our understanding of how nurses' current efforts are part of a continuing commitment to ensuring quality care. DESIGN: Historical research. METHODS: The records of the Nursing Education Committee of the New Zealand Registered Nurses' Association, 1940-1959, and the 309 issues of New Zealand's nursing journal, Kai Tiaki, 1930-1960, were analysed to identify the profession's approach to ensuring best practice. This approach was then interpreted within the international context, particularly Canada and the USA. RESULTS: For nearly 30 years, nurse leaders collaborated in undertaking national surveys of training hospitals requesting information on different nursing practices. They subsequently distributed instructions for a range of procedures and other aspects of nursing care to standardise practice. Standardising nursing care was an effective way to ensure quality nursing at a time when hospital care was delivered mostly by nurses in training. The reasons for and timing of standardisation of nursing care in New Zealand differed from the international move towards standardisation, particularly in the USA. CONCLUSIONS: Historically, nurses also pursued best practice, based on standardising nursing procedures. RELEVANCE TO CLINICAL PRACTICE: Examining the antecedents of the present evidence-based approach to care reminds us that the process and reasons for determining best practice change through time. As knowledge and practice continually change, current confident assertions of best practice should and will continue to be challenged in future.
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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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".