What is a health expectation? Developing a pragmatic conceptual model from psychological theory
Bibliographic record
Abstract
INTRODUCTION: Examination of the existing literature in respect of health expectations revealed both ambiguity in relation to terminology, and relatively little work in respect of how abstract theories of expectancy in the psychological literature might be used in empirical research into the influence of expectations on attitudes and behaviours in the real world. This paper presents a conceptual model for the development of health expectations with specific reference to Alzheimer's disease. METHOD: Literature review, synthesis and conceptual model development, illustrated by the case of a person with newly diagnosed, early-stage Alzheimer's disease, and her caregiver. OUTCOME: Our model envisages the development of a health expectation as incorporating several longitudinal phases (precipitating phenomenon, prior understanding, cognitive processing, expectation formulation, outcome, post-outcome cognitive processing). CONCLUSION: Expectations are a highly important but still relatively poorly understood phenomenon in relation to the experience of health and health care. We suggest a pragmatic conceptual model designed to clarify the process of expectation development, in order to inform future research into the measurement of health expectations and to enhance our understanding of the influence of expectations on health behaviours and attitudes.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".