Influence of goal persistence and goal flexibility on well being in elderly cancer patients and their spouses based on the actor⁃partner inter⁃dependence model
Bibliographic record
Abstract
ObjectiveBased on the actor⁃partner inter⁃dependence model(APIM),to analyze the impact mechanism of goal persistence and goal flexibility on the well⁃being of elderly cancer patients and their spouses.MethodsThe convenient sampling method was used to collect 1 142 elderly cancer patients who were treated in our hospital from January 2019 to June 2023 as the survey objects.And Basic Informaton Questionnaire,the Goal Persistence Scale(TGPS),Flexible Goal Adjustment Scale(FGAS),and Memorial University of Newfoundland Scale of Happiness(MUNSH) were used to investigate.Based on APIM,a structural equation model was established to analyze the impact path of goal persistence and goal flexibility on happiness of elderly cancer patients and their spouses.ResultsThe score of the the Goal Persistence Scale for elderly cancer patients was(38.23±9.31),The score of the Flexible Goal Adjustment Scale was(36.01±10.56),And the score of the Memorial University of Newfoundland Scale of Happiness was(20.82±5.42).The score of the the Goal Persistence Scale for spouse was(33.77±11.31),The score of the Flexible Goal Adjustment Scale was (34.34±10.15).And the score of the Memorial University of Newfoundland Scale of Happiness was (22.53±5.32).The APIM analysis results showed that the goal persistence and goal flexibility of elderly cancer patients positively predicted their well⁃being(both P<0.001).The spouse's goal persistence and goal flexibility of elderly patients with cancer positively predicted their well⁃being(both P<0.001).Both the goal persistence and the goal flexibility of elderly patients with cancer positively predicted spouse well⁃being(both P<0.001).The spouse's goal persistence and goal flexibility of elderly patients with cancer positively predicted patients' well⁃being(both P<0.05).ConclusionsThe elderly cancer patients and their spouses have low goal persistence,goal flexibility and well⁃being on average.Clinical doctors and nurses should pay attention to the psychological status of elderly patients with cancer and their spouses,help them solve their difficulties in life and anti⁃cancer treatment,assist them to obtain resources to adjust their life and treatment goals,and improve their well⁃being.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".