Reliability and validity of Memorial University of Newfoundland Scale of Happiness(MUNSH) in the happiness investigation of the aged with five guarantees in the country
Bibliographic record
Abstract
Objective To explore reliability and validity of Memorial University of Newfoundland Scale of Happiness(MUNSH) in the happiness investigation of the aged with five guarantees in the country.Methods 546 gregarious old people with five guarantees,139 disseminated old people with five guarantees and 131 normal old people in the country were interviewed with multi-stage stratified cluster sampling to calculate the scores of divided measuring tables and total table.The reliability and validity of MUNSH were evaluated by reliability analysis and factor analysis.Results The effect of re-measure of measuring table was better(P≤0.005);there were intrinsic correlation among the items in the measuring table by Hotelling t test(P≤0.005),with better reliability in homogeneity(Cronbach′α quotient 0.802),and better reliability in each divided measuring table(all Cronbach′α quotient 0.65).The correlated quotient among three divided measuring tables were less than α value of each divided measuring table,indicating the better constructive validity of total table.The contribution rate of variance accumulation of all divided measuring tables was about averagely 50%,indicating the poor constructive validity of each divided measuring table.Conclusions Chinese MUNSH has better reliability and content validity,better construction validity in total table,poor construction validity in each divided measuring table,which is basically suited to happiness investigation of the aged with five guarantees in the country.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".