Compassion Fatigue, Satisfaction and Automated Medication Dispenser: A Pilot Mixed-Methods Study
Bibliographic record
Abstract
Abstract Informal caregivers frequently support older adults with complex medication management tasks. However, most informal caregivers are inadequately trained to manage these challenging tasks and may experience caregiver burden. The study aims to evaluate the impact of an automated medication dispenser (AMD) on compassion fatigue, satisfaction, and medication administration hassles. This study recruited 7 pairs of family caregivers and their older care recipients. Caregivers completed the Family Caregiver Medication Administration Hassles Scale (FCMAHS) and the Professional Quality of Life Scale (ProQoL) at baseline, 2 weeks, and 3 months after implementing AMD in care recipients’ homes. Friedman tests showed no significant change in FCMAHS subscale scores over time after Bonferroni correction (α = 0.0125; all p > 0.0125). The total score (primary outcome) was assessed without correction (α = 0.05) and was not significant. Wilcoxon Signed-Rank Tests showed a similar pattern, except for a significant reduction in total score from baseline to 3 months (p = 0.02). Both tests showed no significant change in scores for the subscales of ProQoL after Bonferroni correction (α = 0.0167); all p > 0.0167). Caregivers were interviewed before and after using AMD. The post-intervention interviews were recorded, transcribed, and thematically analyzed. Four themes emerged from the analysis: usability and functionality, experience with remotely delivered pharmacy services, caregiver experience and wellbeing, and impact on caregivers/recipient relationships. The long-term use of AMD has the potential to be beneficial for caregiving burden related to medication management but is influenced by the caregiver’s adjustment period. Future research should verify these pilot findings.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".