Competency assessment tools for infection preventionists: A scoping review
Bibliographic record
Abstract
Background: Infection prevention competencies are critical for successful job performance, career progression and robust performance of infection prevention and control programs. Aim/objective: Identify competency assessment tools available to infection preventionists and describe their characteristics, validation processes and reliability. Methods: A scoping review was conducted on five databases and grey literature from 1999 to 2022. A descriptive synthesis approach was undertaken to analyse the data. Finding/results: Seven tools that meet the inclusion criteria were identified. Of those, one tool was reviewed twice. All tools were developed in the United Kingdom, Canada, China and the United States, and were published between 2009 and 2022. All tools use a rating scale; and the most used method to assess competencies was self-assessment. Levels of competency were cited by five tools. Two tools provided information on validation methods and reliability tests for internal consistency. Discussion: Few competency assessment tools are available in the literature, and there is a lack of information on their development process. A global effort to develop an assessment tool that allows comparison across countries and cultures can be a step forward to propel infection preventionists' careers and enhance the efficacy of Infection Prevention and Control Programs.
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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 | 0.032 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.028 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".