Social Return on Investment Analysis: A Mixed Methods Approach to Assessing the Value of Adult Hospice Services
Bibliographic record
Abstract
OBJECTIVES: Hospice services offer invaluable support to individuals facing life-limiting illnesses; however, quantifying their positive impact presents a challenge. As the demand for palliative care rises because of complex illnesses and an aging population, hospices face the need to prove their value. With funding primarily reliant on charitable donations and limited statutory support, they must demonstrate their effectiveness to secure additional resources in a competitive landscape. METHODS: This study used the Social Return on Investment framework to evaluate the social value generated by four hospice sites offering inpatient and day therapy services across North Wales. Through a mixed-methods approach, quantitative and qualitative data were collected to explore stakeholder experiences, values, and outcomes, facilitating a thorough examination of the broader social impact of hospice care. RESULTS: The average input and output values for the inpatient unit were £602 100 and £1 667 861, respectively, thus returning a base case ratio of £2.77:£1. The day therapy unit had average input and output costs of £155 928 and £1 847 347, respectively, hence a base-case ratio of £11.85:£1. Sensitivity analysis yielded estimates of between £2.20:£1 and £6.83:£1 for the inpatient unit and between £2:44:£1 and £19:51:£1 for the day therapy unit. CONCLUSIONS: As healthcare providers globally confront challenges with resource constraints, adopting value-driven methodologies becomes crucial. Embracing such methodologies fosters a more comprehensive understanding of value, transcending traditional metrics to encompass social, environmental, and long-term sustainability considerations.
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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 | 0.100 | 0.139 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".