Eliciting goal and value-based conversations among the chronic critical illness population in a long-term acute care hospital
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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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".