Why health expectations and hopes are different: the development of a conceptual model
Bibliographic record
Abstract
BACKGROUND: In the literature, 'hope' has often been thought of as an ideal expectation. However, we believe the classification of hope as a type of expectation is problematic. Although both hopes and expectations are future-oriented cognitions, expectations are distinct in that they are an individual's probability-driven assessment of the most likely outcomes, while hopes are an assessment of the most desirable - but not necessarily the most probable - outcomes. AIM: This paper presents a conceptual model of the factors that may serve as common antecedents of hopes and expectations, and a mechanism that may mediate their differentiation. METHOD: Ovid Healthstar and PsycINFO database searches from January 1967 to October 2008 were conducted. An integrative literature review, synthesis and conceptual model development were carried out. Outcome Our model envisages the differentiation of hope from expectation as a dynamic, longitudinal process consisting of three phases: appraisal of possible outcomes, cognitive analysis for achieving hopes and goal pursuit. Key variables such as temporal proximity, controllability, external resources, goals, affect, agency and pathways may moderate the extent of divergence by influencing the perceived probability of achieving desired outcomes. CONCLUSION: Hopes and expectations are distinct, but linked, constructs. This preliminary conceptual model presents how hopes and expectations develop, become differentiated and how social-cognitive factors may moderate this relationship. A better understanding of hopes and expectations may assist health professionals in communicating illness-related expectations while maintaining the integrity of patient hopes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".