Fast thinking: How unconscious bias and binary language contribute to rationing of care to older persons
Bibliographic record
Abstract
BACKGROUND: Binary or categorical thinking is a way of thinking in which the brain unconsciously sorts the masses of information it receives into categories. This helps us to quickly process information and keeps us safe through pattern recognition of possible threats. However, it can also be influenced by unconscious and conscious biases that inform our judgements of other people and situations. OBJECTIVES: To examine nursing practice with older people through the lens of unconscious bias. METHODS: In this critical analysis, using Kahneman's fast and slow thinking, we argue that nurses working with hospitalised older people often rely on thinking quickly in hectic work environments, which can contribute to unconscious and conscious bias, use of binary language to describe older persons and nursing tasks, and ultimately rationing of care. RESULTS: Binary language describes older persons and their care simplistically as nursing tasks. A person is either heavy or light, continent or incontinent, confused or orientated. Although these descriptions are informed in part by nurses' experiences, they also reflect conscious and unconscious biases that nurses hold towards older patients or nursing tasks. We draw on explanations of fast (intuitive) and slow (analytical) to explain how nurses gravitate to thinking fast as a survival mechanism in environments where they are not supported or encouraged to think slow. CONCLUSIONS: Nurses survival efforts in getting through the shift using fast thinking, which can be influenced by unconscious and conscious biases, can lead to use of shortcuts and the rationing of care. We believe that it is of paramount importance that nurses be encouraged and supported to think slowly and analytically in their clinical practice. IMPLICATIONS FOR PRACTICE: Implications Nurses can engage in journaling and reflecting on their practice with older people to examine possible unconscious bias. Managers can support reflective thinking by supporting nurses through staffing models and encouraging conversations about person-centered care in unit practices.
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".