Exploring relationships among distress, psychological growth, motivation, and physical activity among transplant recipients
Bibliographic record
Abstract
PURPOSE: To examine relationships among transplant-specific psychological growth and distress, motivational regulations and health-enhancing physical activity (HEPA) among transplant recipients. METHODS: Participants (N = 138; Mage = 48 years; 58% male), who were primarily heart, liver, lung, and kidney transplant recipients, completed scientifically-supported questionnaires. The associations among transplant-specific emotional health, motivation, and HEPA were examined in a path model. RESULTS: In the path model (Χ(2)(3) = 2.12, RMSEA = 0.02, CFI = 0.98, NNFI = 0.97, SRMR = 0.04), distress was significantly related to introjected regulation and psychological growth was associated with autonomous self-regulation (a combined score of identified and intrinsic regulations), which was a significant correlate of HEPA (R(2)= 0.12). There were no significant direct associations between distress, psychological growth, and HEPA. CONCLUSION: Transplant-specific distress and psychological growth may be factors to target in clinical intervention and rehabilitation. Furthermore, exercise motivation regulations are modifiable factors that relate to HEPA among transplant recipients and could be targeted in the development of rehabilitation strategies aimed at enhancing physical activity in this population. IMPLICATIONS FOR REHABILITATION: Organ transplant recipients should maintain a healthy lifestyle in order to prevent rejection and other risk factors associated with transplantation. Physical activity is a promising lifestyle factor linked to many health benefits. This study shows how a mix of stress and growth following transplantation is related to physical activity motivation and behavior.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".