Applying a Critical Review of an Online Platform for Nurse Practitioner Regulatory Assessment Using a Human Factors Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it