Patient Safety Challenges in the Pandemic: Applying Human Factors Principles to Embed Patient Safety and Experience at the Clinical Front Line
Bibliographic record
Abstract
The COVID-19 pandemic has amplified systemic gaps in patient safety, including social frailty for vulnerable populations such as older adults (Briguglio et al., 2020). Public health measures, such as prolonged and frequent lockdowns, restricted access to visitors and support networks and in extreme cases led to confinement within a single room, The negative impact of these restrictions has focused new attention on social frailty, which has hitherto been a somewhat neglected patient safety issue. Since social frailty is a multifaceted construct, it needs to be considered in a variety of settings and from a range of disciplinary perspectives. This practitioner-led panel consisting of an internist, a human factors engineer, an occupational therapist, and an implementation scientist, examines the impact of the pandemic on social interaction, and social frailty amongst older people in three different settings (hospital, long term care home, community). Through a set of case studies, the panelists provided applied experiences and perspectives on the impact of the pandemic on patient safety and on social frailty in particular. The panel covered theory, and real-world application of human factors principles. The question of how to design safety into the healthcare system were also explored. In this paper we review the problem of social frailty, list some brief case studies and discuss possible intervention strategies.
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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 | 0.023 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".