Promoting the Positive Experience of Caregiving: A Systematic Review of the Positive Aspects of Caregiving Scale
Bibliographic record
Abstract
Abstract While caregivers endure considerable challenges, including potentially deleterious effects on their own well-being, the possibility of positive outcomes for caregivers, such as feeling rewarded and satisfied, has been recognized in scholarly research as well. Caregiving outcomes can be complicated by different aspects of caregiving experiences. Particularly, the Positive Aspect of Caregiving (PAC) scale covers four main positive aspects including caregiving personal gains, motivation for caregiving role, caregiver satisfaction, as well as self-esteem and social aspect of caring. The objective of this study is to summarize the research findings based on the PAC scale and further improve the understanding of the positive experience perceived by caregivers. A systematic literature review was conducted to identify the existing evidences. This systematic review identified empirical research studies written in English focusing on a PAC scale that were published in a peer-reviewed journal between 2004 and 2018. Systematic search was carried out within 10 databases. After careful review of 193 abstracts yielded from the databases, 38 journal articles were identified to have used the PAC scale. Analysis of the selected literature provides four themes that how PAC has been used. The themes are to measure caregiving outcomes, test the effect of caregiver program intervention, complement other caregiving outcome scales, and test the validity in other languages and countries. This systematic review highlights the importance of taking into account of the positive experience of caregiving and further to promote it in a way of buffering the negative outcomes of caregiving.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".