Expert decision making in relation to unanticipated blood glucose levels
Bibliographic record
Abstract
How people (N = 22) with long-standing Type I diabetes make everyday self-care decisions, specifically in regard to unanticipated blood glucose levels (UBGLs) was investigated using grounded theory. Participants differentiated between decisions made in familiar and typical situations and those made in novel situations. Decisions made in familiar situations were straightforward, arising from a confident appraisal of the cause of the UBGL. The primary focus of decision making in response to an UBGL in familiar situations was the decision about the course of action. The focus in unfamiliar situations was the appraisal of the cause of the UBGL. It was characterized by the participants' lack of confidence and by a non-linear progression in which the individual retraced previous phases of the decision-making process or proceeded to tangential steps. Participants matched the features of previously encountered situations to construct a story that explained the events in order to generate some plausible hypotheses. A number of contextual and mediating variables were identified as influencing the decision-making process and the decisions they made. The findings of this research demonstrate that the decision maker's familiarity with the situation influences the nature of the decision-making processes that are used.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".