Validation Evidence for the French Canadian Adaptation of the Multidimensional Fatigue Inventory as a Measure of Cancer-related Fatigue
Bibliographic record
Abstract
Cancer-related fatigue is the most reported symptom among patients with cancer. Researchers in the field of psychooncology have encouraged the development of short instruments, which allow for easier completion by clinical populations while still maintaining solid psychometric properties. The current study examined the validity and reliability of the French Canadian adaptation of the Multidimensional Fatigue Inventory (MFI) among women (n = 277) and men (n = 327) undergoing therapy for breast or prostate cancer, respectively. An exploratory factor analysis of the selected 15-item MFI yielded the following four factors: general/physical, mental, reduced motivation, and reduced activity. This was supported by a confirmatory factor analysis. The reliability, as evaluated by test-retest and Cronbach alpha internal consistency reliability coefficients of the French Canadian shortened MFI, was acceptable. In addition, the four factor-based scores correlated in a theoretically meaningful manner with existing measures of mood disturbance (Profile of Mood States and Hospital Anxiety and Depression Scale), cancer-related stressors (Inventory of Recent Life Experiences), coping with illness (Coping with Health Injuries and Problems Scale), quality of life (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire), and insomnia (Insomnia Severity Index), suggesting good construct, convergent, and concurrent criterion validity. Although further validation is recommended, the results for the French Canadian MFI in assessing cancer-related fatigue in both women and men undergoing cancer treatments showed good psychometric qualities.
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 | 0.026 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".