Older Female Survivors of intimate Partner Violence in the Taiwanese Cultural Context
Bibliographic record
Abstract
Intimate partner violence (IPV) causes severe physical and psychological damage to victims. Older IPV survivors are increasing around the world with the rapidly growing older population. Little is known about the needs of older female IPV survivors in the Chinese cultural context; the aim of this study was to examine the needs of older female IPV survivors in Taiwan. A grounded theory approach was adopted. Individual in-depth interviews were utilized to collect data. First-level coding, constant comparison among coding, then grouping coding into higher levels of categories and identifying a central story were employed to analyze data. Findings were compared to literature for further verification. Eight Taiwanese older female IPV survivors aged 60 to 74 were interviewed. The findings revealed that the cultural premise emphasizing the importance of family was a major contributor to participants’ staying in abusive relationships. Four themes emerged in this study: 1) the marriage serving different purposes throughout the life course of the participants; 2) keeping the family whole enhances life satisfaction in older age; 3) working towards financial independence for lifetime security is an important goal; and 4) finding an independent space as a way of coping with the abusive relationship. Implications for social work practice, in particular incorporating the cultural premise into developing viable options for older female survivors, are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.001 | 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".