Development of an Explanatory Model of Resuscitation Preference Decision Making
Bibliographic record
Abstract
CONTEXT: Establishing resuscitation preferences prior to a medical emergency is a well-recognized component of hospital practice. When done effectively, these help to ensure that care received aligns with patient wishes. In practice however, these conversations can be challenging and influences on choice are not well understood. OBJECTIVES: Objectives: The purpose of this study was to identify influences on patient resuscitation preferences and their relationship to each other, with the aim of developing an explanatory model. METHODS: Constructivist grounded theory was used to analyze 107 clinical notes from a dataset of detail-rich resuscitation preference conversation narratives. Sampling was purposeful and focused on maximum variation. Iterative data collection and analysis and constant comparison was used to enhance rigor as was the incorporation, in later stages of the analysis, of existing theories and models. RESULTS: Twenty-seven coding categories were developed and integrated into the resuscitation preferences conversation model that described the interaction and relationship between influences. Within the model, three categories (Ability to Engage in Meaningful Activity, Trajectory, and Perceptions and Beliefs) informed patient and Substitute Decision Maker (SDM) preferences, while an additional two categories (Social and Knowing, and Experiences) informed substitute decision maker choice. CONCLUSIONS: The developed model builds on prior work and helps explain the relationship between influences and preferences. The integration of both patient and substitute decision maker perspective into the model shows the complexity of the substitute decision maker role in decision-making. The model should be used in conjunction with existing conversation guides to support effective resuscitation preference conversations.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".