Applying the triple bottom line of sustainability to healthcare research—a feasibility study
Bibliographic record
Abstract
OBJECTIVE: The triple bottom line (TBL) of sustainability is an important emerging conceptual framework which considers the combined economic, environmental and social impacts of an activity. Despite its clear relevance to the healthcare context, it has not yet been applied to the evaluation of a healthcare intervention. The aim of this study was to demonstrate whether doing so is feasible and useful. DESIGN: Secondary data analysis of a 12-month randomized controlled trial. SETTING: Community based mental health care. PARTICIPANTS: Patients with chronic psychotic illnesses (n = 333). INTERVENTION(S): Community treatment orders. MAIN OUTCOME MEASURE(S): Financial and environmental (CO2 equivalent) costs of care, obtained from healthcare service use data, were calculated using publicly available standard costs; social sustainability was assessed using standardized social outcome measures included in the trial data. RESULTS: Standardized costing and CO2e emissions figures were successfully obtained from publicly available data, and social outcomes were available directly from the trial data. CONCLUSIONS: This study demonstrates that TBL assessment can be retrospectively calculated for a healthcare intervention to provide a more complete assessment of the true costs of an intervention. A basic methodology was advanced to demonstrate the feasibility of the approach, although considerable further conceptual and methodological work is needed to develop a generalizable methodology that enables prospective inclusion of a TBL assessment in healthcare evaluations. If achieved, this would represent a significant milestone in the development of more sustainable healthcare services. If increasing the sustainability of healthcare is a priority, then the TBL approach may be a promising way forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.116 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".