Evaluating the Positive Experience of Caregiving: A Systematic Review of the Positive Aspects of Caregiving Scale
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: As attention to positive caregiving experience increases, there is growing evidence concerning how the identification of the positive aspects of caregiving can be beneficial in supporting caregivers. The purpose of the current study is to review the literature where the Positive Aspects of Caregiving Scale (PACS) was used, identify the ways studies have used the PACS, and summarize the relationship between PACS and the contextual factors as well as outcomes of caregiving. RESEARCH DESIGN AND METHODS: A systematic literature review was conducted. Electronic databases were searched, and empirical research studies written in English that were published in a peer-reviewed journal after 2004 were identified. After a careful review of the 194 abstracts yielded from the databases and the reference lists of the associated articles, 52 eligible studies were identified, and relevant findings were extracted. RESULTS: Some commonality in terms of how studies have used the PACS emerged. The literature reviewed was further grouped into 3 categories depending on whether the study tested the PACS as a valid and reliable measurement, examined the PACS as outcomes of caregiving, or as a predictor of certain outcomes. DISCUSSION AND IMPLICATIONS: This review suggests that PACS is utilized for multiple purposes and yields considerable evidence supporting the importance of understanding the positive experience of caregiving. However, there is limited adaptation of the PACS in a large survey, and studies were heavily focused in the United States with little evidence from other countries. Further studies to address these limitations will be needed.
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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 | 0.025 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".