Implementation of the Serious Illness Care Program on Hospital Medical Wards
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background Poor communication with hospitalized patients facing serious, life-limiting illnesses can result in care that is not consistent with patients’ values and goals. The Serious Illness Care Program (SICP) is a communication intervention originally designed for the outpatient oncology setting that could address this practice gap. Methods A multihospital quality improvement initiative adapted and implemented the SICP on the medical wards of four teaching hospitals in Calgary, Hamilton, Ottawa, and Montreal. The SICP consists of three main components: tools (including the Serious Illness Conversation Guide for clinicians), training for frontline clinicians to practice using the Guide, and system change to trigger and support serious illness conversations in practice. Implementation of the SICP at each site followed a phased approach: (1) Building a Foundation; (2) Planning; (3) Implementation; and (4) Sustainability. To assess the success of implementation and its impact, we developed an evaluation framework that includes process measures (e.g., number and proportion of eligible clinicians trained, number and proportion of eligible patients who received a serious illness conversation), patient-reported outcomes (including a validated, single-item “Feeling Heard and Understood” question), and clinician-reported outcomes. Conclusion Based on our adaptation and implementation efforts to date, we have found that the SICP is readily adaptable to an inpatient medical ward setting. Future manuscripts will report on the fidelity of implementation, impact on patient- and clinician-reported outcomes, and lessons learned about how to implement and sustain the program.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it