Psychometric properties of Hope Scales: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Hope is recognised as an important factor in health, illness, and well-being. Many scales to measure hope have been developed and used in various disciplines, yet, their psychometric properties have not been systematically reviewed. AIM: To systematically review the psychometric properties of hope scales. DESIGN: Systematic review. METHODS: Four electronic databases were searched followed by a hand search. The data were extracted and qualitatively evaluated by the COSMIN checklist, an instrument designed as a quality rating tool for systematic reviews of psychometric properties. RESULTS: From 1271 retrieved abstracts, 68 papers met the inclusion criteria. The most used scale was the Snyder Hope Scale (46%) followed by the Herth Hope Index (16%). All other scales (n = 16) were evaluated in less than 10% of the papers. Structural validity (91%), internal consistency (88%), and hypothesis testing (74%) were the most reported properties. Reliability (34%), cross-cultural validity (34%), content validity (25%), and criterion validity (15%) were reported in less than 50% of the papers. Only two (3%) studies reported responsiveness, and none reported measurement error. Less than 35% of the validation studies achieved excellent or good quality for any of the measurement properties. CONCLUSION: The results show that no robust and valid scale exists for measuring hope. It highlights important gaps in psychometric properties of hope scales. Despite more than 40 years of research and development of hope scales, the currently available scales do not meet the standards of psychometric evaluation. This calls for efforts to improve the quality of hope scales.
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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 | 0.038 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".