Sustaining Clinical Programs During Difficult Economic Times: A Case Series from the Hospital Elder Life Program
Bibliographic record
Abstract
OBJECTIVES: To explore strategies used by clinical programs to justify operations to decision-makers using the example of the Hospital Elder Life Program (HELP), an evidence-based, cost-effective program to improve care for hospitalized older adults. DESIGN: Qualitative study design using 62 in-depth, semistructured interviews conducted with HELP staff members and hospital administrators between September 2008 and August 2009. SETTING: Nineteen HELP sites in hospitals across the United States and Canada that had been recruiting patients for at least 6 months. PARTICIPANTS: HELP staff and hospital administrators. MEASUREMENTS: Participant experiences sustaining the program in the face of actual or perceived financial threats, with a focus on factors they believe are effective in justifying the program to decision-makers in the hospital or health system. RESULTS: Using the constant comparative method, a standard qualitative analysis technique, three major themes were identified across interviews. Each focuses on a strategy for successfully justifying the program and securing funds for continued operations: interact meaningfully with decision-makers, including formal presentations that showcase operational successes and informal means that highlight the benefits of HELP to the hospital or health system; document day-to-day, operational successes in metrics that resonate with decision-maker priorities; and garner support from influential hospital staff that feed into administrative decision-making, particularly nurses and physicians. CONCLUSION: As clinical programs face financially challenging times, it is important to find effective ways to justify their operations to decision-makers. Strategies described here may help clinically effective and cost-effective programs sustain themselves and thus may help improve care in their institutions.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".