System-Level Factors Affecting Long-Term Care Wait Times: A Scoping Review
Bibliographic record
Abstract
Waitlists for long-term care (LTC) continue to grow, and it is anticipated aging populations will generate additional demand. While literature focuses on individual-level factors, little is known about system-level factors contributing to LTC waitlists. We considered these factors through a scoping review. Inclusion/exclusion included publication year (2000-2022), language, paper focus, and document type. A total of 815 abstracts were identified, only 17 studies were included. Through qualitative content analysis, 10 key factors were identified: (1) waitlist management styles, (2) inconsistent standards of admission, (3) personnel shortage, (4) insufficient community-based care, (5) inequitable distribution of services, (6) lack of system integration, (7) unintended consequences of insurance plans, (8) ranking preferences, (9) the debate of supply and demand, and (10) financial incentives. Targeting interventions to address waitlist management, community-based care capacity, and demographic trends could improve access. More research is needed to address system-level barriers to timely LTC access.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".