Development and Usability Evaluation of a Desktop Software Application for Pain Assessment in Infants
Bibliographic record
Abstract
Background Pain assessment is a key component of pain management and research in infants. We developed software to assist in coding of pain in infants called PAiN (Pain Assessment in Neonates). Aims The aims of this study were to evaluate the usability of PAiN in terms of effectiveness, efficiency, and satisfaction among novice and expert users and to compare the efficiency and satisfaction of PAiN to existing software for coding of infant pain among expert users. Methods A quantitative usability testing approach was conducted with two participant groups, representing novice and expert end-users. Testing included an observed session with each participant completing a pain assessment coding task, followed by administration of the Post Study System Usability Questionnaire and Desirability Toolkit. For comparison, the usability of existing coding software was also evaluated by the expert group. Results Twelve novice and six expert users participated. Novice users committed 14 noncritical navigational errors, and experts committed six. For experts, the median time for completing the coding task was 28.6 min in PAiN, compared to 46.5 min using the existing software. The mean Post Study System Usability Questionnaire score among novice (1.89) and expert users (1.40) was not significantly different (P = 0.0917). Among experts, the score for the existing software (4.83) was significantly (P = 0.0277) higher compared to PAiN (1.40). Lower scores indicate more positive responses. Conclusions Users were highly satisfied with PAiN. Experts were more efficient with PAiN compared to the existing software. The study was critical to ensuring that PAiN is error free and easy to use prior to implementation.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".