Psychometric validation of the Multidimensional Acceptance of Loss Scale
Bibliographic record
Abstract
OBJECTIVE: To develop and field test the Multidimensional Acceptance of Loss Scale to measure disability acceptance based on the four value changes identified by Beatrice Wright. DESIGN: Quantitative descriptive design using exploratory factor analysis to determine the factorial validity of the Multidimensional Acceptance of Loss Scale. SETTING: The Canadian Paraplegic Association. SUBJECTS: One hundred and sixty-one members of the Alberta, Saskatchewan, Nova Scotia and Manitoba chapters of the Canadian Paraplegic Association were recruited for the current study. RESULTS: A four-factor structure accounting for 50% of the total variance was found for the Multidimensional Acceptance of Loss Scale. The internal consistency reliability coefficients (Cronbach's alpha) for the four factors ranged from 0.80 to 0.88. Three clusters of participants with high, moderate and low disability acceptance were identified based on their profiles of Multidimensional Acceptance of Loss Scale subscale scores using cluster analysis. MANOVA results indicated that participants in the three clusters significantly differed on self-esteem, F(2, 154) = 19.78, P < 0.001 and quality of life, F(8, 236) = 5.16, P < 0.001. Participants with high Multidimensional Acceptance of Loss Scale scores have higher self-esteem and quality of life scores than those with lower scores. CONCLUSION: The Multidimensional Acceptance of Loss Scale was found to measure the four value changes in Beatrice Wright's disability acceptance theory in a sample of Canadians with spinal cord injuries. It demonstrated good internal consistency reliability and construct validity.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".