Applying a Critical Review of an Online Platform for Nurse Practitioner Regulatory Assessment Using a Human Factors Approach
Bibliographic record
Abstract
Using human factors approaches such as usability and usefulness in the evaluation of computerized information systems is key to the successful adoption for end users. Usability is associated with measuring the ease of use of a system, whereas usefulness is concerned with the accuracy and currency of the system content. At the British Columbia College of Nurses and Midwives, the nurse practitioner peer review incorporates the use of an online platform as part of the assessment process. The technology within this system has experienced challenges since its original deployment in 2012, particularly from an end user perspective. As such, it was important to conduct an evaluation in order to clearly identify the issues and develop recommendations and requirements for enhancements and improvements. A recognized usability evaluation methodology was selected including usability inspection as well as usability testing to ensure a thorough and comprehensive approach to this work. This approach proved highly effective in uncovering system issues within the regulatory health professions domain. Overall, usability evaluation methods were integral to meeting the objectives of this article and in demonstrating the value of applying human factors approaches in this context.
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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 | 0.120 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.028 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 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".