Punctuated Entropy in the ICU During COVID-19: Team Nursing and Burnout
Bibliographic record
Abstract
BackgroundThe novel demands on hospital capacity arising from the COVID-19 pandemic revealed already-existing systemic weaknesses. Intensive care units experienced a sustained surge capacity and were forced to introduce modified standards of care and practices.PurposeIn this article we use punctuated entropy as a conceptual lens to reveal the impact of the COVID-19 pandemic on Ontario hospitals by drawing attention to the cumulative impact of repeated disaster events on their capacity to recover.MethodsThis qualitative instrumental case study took place at a Medical-Surgical Intensive Care Unit in a university-affiliated teaching community hospital in a large urban center in Ontario, Canada. Twelve healthcare professionals from the ICU participated in in-depth semi-structured interviews.ResultsIn-depth interviews with healthcare providers revealed an already-vulnerable system and the disproportionate impact of COVID-19 on the nursing workforce, compounding pre- burnout and compassion injury.ConclusionThe structure of intensive care and the dynamics of collaborative practices within ICUs are subject to continual reconfiguration, potentially leading to punctuated entropy - a permanent state of a lack of capacity to recover. Disaster recovery planning in healthcare services delivery should not be focussed simply on navigating the 'temporary' effects of a single event, but rather on how the event interacts with the already existing 'pathological' state of the healthcare system. In this way solutions to longitudinal systemic problems in ICU healthcare delivery can be anticipated and plans for mitigation can be put in place.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".