Using a Delphi technique to develop infection control competencies for new graduates in Australia and Taiwan
Bibliographic record
Abstract
The aim of this study is to establish the essential infection control competencies required for newly graduating nurses in Australia and Taiwan by using a modify Delphi technique. One hundred and twenty-two participants from clinical and educational backgrounds in both countries were invited to participate in the study. A questionnaire outlining possible areas of competency, based on a similar study in Canada was developed for the Delphi survey. Participants were asked to indicate how highly they rated each competency area using a 5 point likert scale, in terms of their applicability and importance for newly graduating nurses. An expert reference group helped to validate the original questionnaire. Respondents were also asked to identify any gaps in the competencies outlined in the questionnaire. Quantitative data were analyzed using SPSS 15.0 and qualitative data collected were content analyzed with similar items combined into themes. This research was approved by the Human Research Ethics Committee at the University of Wollongong prior to the commencement of any data collection. In the first round, 93 experts returned completed questionnaires. The first round data demonstrated that 71% items were successful in reaching the majority of agreement. Items that had not achieved consensus and additional items identified by the Delphi panellists from the first round were included in a second round in November 2008. Participants were asked to use the same rating system. It is envisaged that after completing two rounds of this survey, we will have obtained a consensus of expert opinion on essential infection control competencies for newly graduating nurses in Australia and Taiwan. The results of this study are expected to make a contribution to future curriculum planning in nursing programmes as well as improve Infection Control standards in health facilities in Australia and Taiwan, with obvious benefits for patients and society more generally.
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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 | 0.052 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".