PLANNING FOR FUTURE AGING IN THE FAMILY CONTEXT
Bibliographic record
Abstract
The demographic wave of baby boomers makes planning for future aging a priority. Early life cycle planning and the failure of individuals to plan were cited as major policy issues related to global aging (Lee, Mason, & Cotlear, 2010). Although studies have investigated individual differences in planning for future care (e.g., age, gender), far fewer have examined planning in the context of families. This study examined whether future care discussions within families and perceived family support predicted planning actions. The sample was comprised of 385 adults, aged 50 and older (mean age=66.5, SD=9.3, range=50–92). Participants completed a questionnaire, either in-person or on-line, that included demographic questions, the Discussion of Future Care Needs Scale (DFCNS; Fowler, 2006), the Multidimensional Scale of Perceived Social Support: Family (MSPSS:F; Zimet et al., 1988) and T/F items from the Associated Press-NORC Center long-term care poll that pertain to specific planning actions (e.g., residential downsizing). A hierarchical linear regression entered two established predictors (age, future income security) of planning in the first block and the DFCNS and MSPSS:F in the second block. Age and future income security explained 22% of the variance in planning. The addition of the DFCNS and MSPSS:F explained 31% of the variance and the R2 was significant (p<.001). DFCNS was a significant predictor (p<.001) of planning; however, the MSPSS:F was not. These results suggest that family discussions are important for promoting future care planning. Strategies for facilitating these discussions within families will be presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".