Surviving Vulnerabilities of Isolation among Widowed Empty Nesters
Bibliographic record
Abstract
A sufficient number of empty nesters living in isolation had been increasing in population, thus it is encouraged to provide a plethora of research data and studies on gerontology and isolation that could contribute to their wellbeing. This study utilized the descriptive phenomenological analysis and purposive sampling method to determine the five participants. Inclusion criteria were established to narrow down participants with common conditions. Data were gathered through online interviews and analyzed using Lichtman’s 3 C’s comprised of codes, categories, and concepts. The following themes emerged: economic vulnerability, physical vulnerability, social vulnerability, emotional vulnerability, coping with isolation vulnerabilities, and hopeful aspirations. In conclusion, empty nesters may experience multiple challenges that made them vulnerable in many aspects, but they were also able to develop coping strategies to manage these vulnerabilities. The limitations encountered in this study may be improved by exploring the experiences of empty nesters from other socio-economic categories and conducting a mixed-method study that would generate a broad range of data. The result of the study exposes various vulnerabilities that contribute to challenges encountered by empty nesters. Since the results are limited from generalizing the entire senior citizen population, it could be noted that the resiliency to survive challenging situations promote a holistic approach for aging and positive psychology as results provided varied sources for coping which ranges from both the internal and external sources. Survival elements of connectedness and transcending difficult situations affirm the practicality of promoting life meaning especially during difficult situations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".