Information Overload—Do We Read All the Posters Displayed Across the Walls on Hospital Wards?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To establish whether posters displayed across the walls on hospital wards are read, what information is important, and how the information should be received. METHODS: Sixty-eight staff and 32 patients' relatives were interviewed across 3 older people's medical wards followed by 20 follow-up secondary questionnaires postintervention. RESULTS: Only 23% of those interviewed were able to recall any of the posters displayed, and of those, 34% did not find the information useful. Those interviewed were enthusiastic about utilizing alternative media. A quarter felt the walls across the hospitals wards should be for artwork. Among patients' relatives interviewed, common information requests were "the discharge pathway," "delirium," and "falls." Based on the initial findings, a targeted information board was installed and a mural was painted across the wall in one of the wards. Further post-intervention interviews with patients' relatives showed that the board was well received, but further unmet information needs were uncovered. Despite the new mural, 45% called for more paintings. CONCLUSIONS: Most people ignore the posters displayed across the walls of hospital wards, and unmet information needs are rife. An appetite exists for alternative media. Paintings were earnestly called for, highlighting how a comforting environment could be part of the holistic care we offer patients in hospital.
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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 | 0.010 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".