Eliciting goal and value-based conversations among the chronic critical illness population in a long-term acute care hospital
Why this work is in the frame
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Bibliographic record
Abstract
In the United States, goal and value-based conversations between healthcare professionals and patients experiencing chronic critical illness (CCI) in a long-term acute care hospital (LTACH) do not occur routinely as part of the standard of care, leading to a poor quality of life and increased levels of stress, anxiety, and depression among this patient population (Kahn et al., 2015; Lamas et al., 2017a; Lamas et al., 2017b). Since the Theory of Planned Behavior is designed to both explain and predict behavior in specific contexts, such as healthcare professionals’ intentions and behavior to have goal and valued-based conversations with this patient population (Ajzen & Fishbein, 2005), and the literature supports the use of semi-structured interview tools to do so with this patient population (Chochinov et al., 2015; Johnston et al., 2015; Lamas et al., 2017a), this doctoral capstone aims to enhance patient-reported outcomes among this patient population by providing healthcare professionals, specifically occupational therapists, with the most useful semi-structured interview tool (i.e., the Canadian Occupational Performance Measure [COPM]) to facilitate goal and value-based conversations more routinely. The COPM is client-centered OT semi-structured interview tool designed to generally (1) elicit goal and value-based conversations; (2) guide collaborative goal-setting; and (3) measure patient-reported outcomes (Law et al., 2005). The results indicate both clinical and statistical significance over time across patients for the patient-reported outcomes, self-perceived performance and satisfaction, demonstrating support for the establishment of routine goal and value-based conversations as part of the standard of care between healthcare professionals and this patient population.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it