Comprehensive Assessment of Critical Care Needs in a Community Hospital*
Bibliographic record
Abstract
OBJECTIVE: To design and implement a needs assessment process that identifies gaps in caring for critically ill patients in a community hospital. DESIGN, SETTING, SUBJECTS: This mixed-method study was conducted between June 2011 and February 2012. A conceptual framework, centered on the critically ill patient, guided the design and selection of the data collection instruments. Different perspectives sampled included regional leaders, healthcare professionals at the community hospital and its referral hospital, as well as family members of patients who had received care at the community ICU. Data sources included interviews (n = 22), walk-throughs (n = 5), focus groups (n = 31), database searches, context questionnaires (n = 8), family surveys (n = 16), and simulations (n = 13). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Nine needs were identified. At the community hospital, needs identified included lack of access to human resources, gaps in expertise, poor patient flow and ICU bed use, communication, lack of educational opportunities, and gaps in end-of-life care and interprofessional teamwork. Needs were also identified in the interhospital interaction between the community and referral hospitals, which included an inadequate hospital network and gaps in transfer and repatriation of patients. The methodology uncovered the causes and widespread impact of each need and how they interacted with one another. Proposed solutions by the participants are presented including both organizational and educational/clinical solutions. CONCLUSIONS: This study captured needs in a complex, interprofessional, interhospital context, which can be targeted with tailored interventions to improve patient outcomes in a community hospital. Furthermore, this study provides a preliminary framework and rigorous methodology to performing a needs assessment in this setting.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".