Bibliographic record
Abstract
Planning for future care needs is an important but often neglected aspect of aging well. Decisions about care are often made reactively after a catastrophic health event, leading to significant stress for older adults and their family members. The purpose of this study was to use a well-established theory of behavior, the Theory of Planned Behavior (TPB), to predict planning, including the avoidance of planning and stages of planning behavior. Predictors from the theory (i.e., attitudes, norms, perceived control) were combined with other variables supported by the literature (i.e., age, future self-continuity, perceived future financial status, work experience with older adults, caregiving experience, contact with nursing homes) to determine who is most likely to engage in planning behavior, and who may be at risk for lack of planning. A sample of 83 participants (aged 18–70) completed questionnaires assessing these variables. Hierarchical regression analyses, with age entered in step 1, were used to determine which factors predicted overall planning, the avoidance of planning, and four stages of planning (awareness, gathering information, decision-making, and concrete planning). Taken together, these predictors explained a significant amount of variance in planning behavior, for example, 64% of the variance in concrete planning. Moreover, TPB predictors (attitudes, norms, and perceived control) were significant predictors of all stages of planning. These findings suggest that: a) the TPB has utility in predicting who is most likely to plan across a wide age range, and b) interventions designed to promote planning should target predictors of planning (e.g., perceived control).
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".