Cognitive impact assessment of electronic knowledge resources: a mixed methods evaluation study of a handheld prototype.
Bibliographic record
Abstract
RATIONALE: We recently proposed a new method to systematically assess the cognitive impact of knowledge resources on health professionals. OBJECTIVE: To describe promises and shortcomings of a handheld computer prototype of this method. BACKGROUND: We developed an impact scale, and combined this scale with a Computerized Ecological Momentary Assessment technique. METHOD: We conducted a mixed methods evaluation study using a 7-item scale within a questionnaire linked to a commercial knowledge resource. Over two months of Family Medicine training, 17 residents assessed the impact of 1,981 information hits retrieved on handheld computer. From observations, log-reports, archives of hits and interviews, we examined issues associated with hardware, software and the questionnaire. FINDINGS: Fifteen residents found the questionnaire clearly written, and only one pointed to the questionnaire as a major reason for their low level of use of the resource. Residents reported technical problems (e.g. screen trouble) or limitations (e.g. limited tracking function) and socio-technical issues (e.g. software dependency). CONCLUSION: Lessons from this study suggest improvements to guide future implementation of our method for assessing the cognitive impact of knowledge resources on health professionals.
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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 | 0.038 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".