Uncovering the Value of a Historical Paper-Based Collaborative Artifact: The Nursing Unit's Kardex System
Bibliographic record
Abstract
We identify useful functions and usability characteristics of a historical cognitive artifact used by nurses working in a hospital unit, the Kardex. By identifying aspects of a widely used artifact, we uncover opportunities to improve the usefulness of current systems for hospital nurses. We conducted semi-structured interviews with registered nurses about their prior experience with the Kardex. Questions included what elements of the Kardex are missing from their current electronic support. Memos were generated iteratively from interview transcript data and grouped into themes. Eighteen nurses from multiple clinical areas participated and had a median of 25-29 years of nursing experience. The themes were: (1) a status at a glance summary for each patient, (2) a prospective memory aid, (3) efficiency and ease of use, (4) updating information required to maintain value, (5) activity management, (6) verbal handover during shift-to-shift report, (7) narrative charting and personalized care, and (8) non-clinical care communication. Implications for digital support are to provide immediate, portable access to a standardized patient summary, support for nurses to manage their planned activities during a series of shifts, provide unstructured text fields for narrative charting, and to support adding informal notes for personalized care.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".