Scarcity
Bibliographic record
Abstract
OBJECTIVES: Clinicians' perceptions of scarcity influence rationing of critical care resources, which may lead to serious adverse outcomes for patients who are denied access. We sought to better understand the phenomenon of scarcity in the critical care setting. DESIGN: Qualitative research methods. We used purposeful sampling to recruit ICU clinicians who were frequently involved in decisions to allocate ICU resources. Thematic analysis was performed to identify concepts related to the phenomenon of scarcity. SETTING: An ICU of a university-affiliated hospital in Toronto, Canada, between October and December 2007. SUBJECTS: We conducted 22 interviews with 12 ICU physicians, 4 ICU fellows, 2 ICU nursing team leaders, and 4 ICU resource nurses. MAIN RESULTS: The perception of scarcity arose from a complex interaction of factors within the institution including: 1) practices of non-ICU physicians (e.g., failure to specify end-of-life treatment plans or to secure an ICU bed prior to elective high-risk surgery), 2) family demands for life support and clinicians' perception of a lack of legal support if they opposed these, and 3) inability to transfer patients to non-ICU care settings in a timely manner. Implications of scarcity included: 1) diversions of critically ill patients, 2) premature patient transfers, 3) temporary delivery of critical care in non-ICU locations (e.g., emergency department, postanesthesia care unit), and 4) interprofessional conflicts. CONCLUSIONS: ICU clinicians' perceptions of scarcity may lead to rationing of critical care resources. We found that nonmedical factors strongly influenced prioritization activity, both for admission and discharge. Although scarcity of ICU beds might be mitigated by process improvements such as patient flow or proactive communication, our findings highlight the importance of a fair process for inevitable limit setting at the bedside.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.156 | 0.031 |
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".