Implementation of the Family HELP Protocol: A Feasibility Project for a West Texas ICU
Bibliographic record
Abstract
The purpose of this project was to determine if bedside intensive care unit (ICU) nurse buy-in to the Family Hospital Elder Life Program (HELP) protocol was sufficient to make implementation feasible at one county hospital in West Texas. Surveys were anonymous with ballot box collection being available to the bedside ICU nurses for one week each. Questions were based on literature findings of expected outcomes, identified barriers and facilitators, Calgary Family Intervention Method framework domains, and the Centers for Disease Control and Prevention Framework for program evaluation. Outcome measures were taken from the stated aims of the project and evaluated from paired baseline and summative survey questions. Survey participation was approximately half of nurses employed in the studied ICU. Analysis of the surveys showed a positive perception of family presence decreasing patient delirium symptoms, and a positive perception of the Family HELP protocol. The results described a high perception of family members as partners in care and high intention to implement the Family HELP protocol, indicating strong support of a full implementation of the protocol. The high level of bedside nurse buy-in present in this study has large implications for successful implementation of the Family HELP protocol in the near future, with sustainability and continued use supported by potential inclusion of the task in the electronic health record charting.
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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 | 0.036 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".