PLANNING FOR FUTURE CARE NEEDS: THE IMPORTANCE OF PERCEIVED NEED
Bibliographic record
Abstract
Abstract Despite the demonstrated need to plan for future care needs, many individuals fail to engage in planning, often with negative consequences for their future health and well-being (Lee, Mason, & Cotlear, 2010). Theoretically, the propensity to utilize planning resources may be related to the perceived need for care in the future, a demonstrated predictor of the utilization of health and mental health services (Andersen, 1995; Karlin, Duffy, & Greaves, 2008). The purpose of this study was to examine perceptions of need for future care in combination with predisposing (age, financial security, attitudes towards planning) and enabling (anticipated support, satisfaction with family discussions about future care needs) variables in predicting planning behavior. The sample was comprised of 385 adults, aged 50 years and older (M=66.5, SD=9.3, range=50-92). Hierarchical regression analyses entered two well-established predictors, age and financial security in step 1, and attitudes towards planning, anticipated support, satisfaction with family discussions, and perception of need in step 2. Age and financial security explained 17% of the variance in planning; the addition of step 2 variables explained 33% of the variance and R-squared was significant (p<.001). All predictors were significant at p<.001, with the exception of anticipatory support (p<.05). These results support both the individual (i.e. positive attitudes, perceived need) and contextual nature of planning, in particular the belief that support will be available when you need it and the benefits of family discussions in facilitating planning. Recommendations for enhancing successful planning among individuals and their families will be presented.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".