Bibliographic record
Abstract
Objective: As the eighth leading cause of death in the US, pneumonia (PN) is relevant to the health of the elderly and young. Accountability for readmission is part of the Affordable Care Act’s Hospital Readmissions Reduction Program (RRP), which levies penalties for readmissions. We examined communication using framing effects which can motivate patients’ decisions collaboratively with providers for post discharge care and readmissions prevention. Communication strategies (CS) can facilitate decision-making (DM) about health care choices. The project’s aims were to (1) compare CS of framing effects (positive or negative messages) on the readmission outcome 30 days post discharge; (2) assess PN readmissions decrease 30 days post discharge when CS include the patient/family in decisions about transitions; (3) determine the impact of between patients and HCPs agreement for post hospital care, and (4) examine confounding effects between framing effects and readmission rates of age, PN severity index (PSI), and the number of diagnoses.Methods: A double-blind randomized control trial (RCT) used parallel assignment of 153 PN patients to one of three arms to test the communication framing effects using power analysis, odds ratio, Fischer’s exact and ANOVA. Arm A was the Intervention positive framing group (n = 44), arm B was the Intervention Negative framing group (n = 65), and arm C was the control group (n = 44).Conclusions: Findings suggest that framed messages aid in the reduction of PN readmission rates in hospitals. DM strategies incorporates education and understanding of risk by the patient, so the healthcare teams can encourage and improve readmission outcomes.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".