The Use of Partnered Usability Testing to Help to Identify Gaps in Online Work Flow
Bibliographic record
Abstract
The Hospital for Sick Children developed a web based referral process to replace their current paper based referral system for the acceptance and management of referrals from community pediatricians and other health care professionals. Partnered usability testing, involving an external usability consultant and an internal project manager (i.e., a nurse at the hospital), familiar with the referral process as well as the prototype functionality was used to assess the prototype before completing the final development stage. The partnered usability sessions were a unique way to deal with the usability test of a very complex application that must accommodate multiple user groups as well as multiple and interlinked workflows. This approach was very successful in identifying ways in which the application did not capture some of the more fine grained details of work flow. The detection of some of the subtleties related to workflow would not have been possible without the participation of the internal project manager who was familiar with the minutia of the paper referral process in addition to the detailed functions of the prototype and its “missing” functionality. Details of the roles and steps involved in partnered usability testing are discussed as well as keys to successful 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 | 0.048 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".