Toward systematic reviews to understand the determinants of wait time management success to help decision-makers and managers better manage wait times
Bibliographic record
Abstract
BACKGROUND: Long waits for core specialized services have consistently been identified as a key barrier to access. Governments and organizations at all levels have responded with strategies for better wait list management. While these initiatives are promising, insufficient attention has been paid to factors influencing the implementation and sustainability of wait time management strategies (WTMS) implemented at the organizational level. METHODS: A systematic review was conducted using the main electronic databases, such as CINAHL, MEDLINE, and Cochrane Database of Systematic Reviews, to identify articles published between 1990 and 2011 on WTMS for scheduled care implemented at the organizational level or higher and on frameworks for analyzing factors influencing their success. Data was extracted on governance, culture, resources, and tools. We organized a workshop with Canadian healthcare policy-makers and managers to compare our initial findings with their experience. RESULTS: Our systematic review included 47 articles: 36 related to implementation and 11 to sustainability. From these, we identified a variety of WTMS initiated at the organizational level or higher, and within these, certain factors that were specific to either implementation or sustainability and others common to both. The main common factors influencing success at the contextual level were stakeholder engagement and strong funding, and at the organizational level, physician involvement, human resources capacity, and information management systems. Specific factors for successful implementation at the contextual level were consultation with front-line actors and common standards and guidelines, and at the organizational level, financial incentives and dedicated staffing. For sustainability, we found no new factors. The workshop participants identified the same major factors as found in the articles and added others, such as information sharing between physicians and managers. CONCLUSIONS: Factors related to implementation were studied more than those related to sustainability. However, this finding was useful in developing a tool to help managers at the local level monitor the implementation of WTMS and highlighted the need for more research on specific factors for sustainability and to assess the unintended consequences of introducing WTMS in healthcare organizations.
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 | 0.286 | 0.596 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.036 | 0.029 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.018 | 0.031 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".