Is Sustainability Possible? A Review and Commentary on Empirical Studies of Program Sustainability
Bibliographic record
Abstract
An important final step in the life cycles of programs and their evaluation involves assessing new programs’ or innovations’ sustainability. This review and synthesis of 19 empirical studies of the sustainability of American and Canadian health-related programs examines the extent of sustainability achieved and summarizes factors contributing to greater sustainability. Three definitions for measuring sustainability were examined: continued program activities (18 studies), continued measured benefits or outcomes for new clients (2 studies), and maintained community capacity (6 studies). Methods of studying sustainability were also assessed. In 14 of 17 studies covering the continuation of program activities, at least 60% of sites reported sustaining at least one program component. Although these studies’ methods had substantial limitations, cross-study analysis showed consistent support for five important factors influencing the extent of sustainability: (a) A program can be modified over time, (b) a “champion” is present, (c) a program “fits” with its organization’s mission and procedures, (d) benefits to staff members and/or clients are readily perceived, and (e) stakeholders in other organizations provide support.
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 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 | 0.048 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".