Communication Vulnerability within Singapore’s Healthcare Environment
Bibliographic record
Abstract
Communication difficulties can, and often do, create barriers between patients and healthcare workers (HCWs). We examined the perceptual differences between patients and caregivers; and HCWs with regards to their perceived communication vulnerabilities and identified communication needs in a tertiary hospital. A survey was conducted in selected outpatient settings among patients, their caregivers and HCWs, in a cross-sectional study. Respondents rated the reasons and frequency of encountering the communication difficulties during a hospital visit. Fifty-four percent of patients and caregivers cited poor hearing in the presence of noise, while HCWs cited patient’s poor vision (87%) as their primary communication barrier that requires improvement. Majority of HCWs (90%) had encountered patients who presented multiple communication barriers a quarter of the time. A third of HCWs felt that such encounters were especially challenging during communication, with very limited strategies available to deal with such communication vulnerable individuals. Patients, caregivers and HCWs universally experience communication challenges, even if their perceived barriers to communication happen to differ. Such perceptual difference between patients and HCWs may lead to inconsistent use of communication strategies by HCWs, potentially compromising patient’s healthcare needs. Nonetheless, the onus is on healthcare providers to bridge this communication gap to improve patient 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".