Dental hygiene shortages and their impact on the Canadian Dental Care Plan.
Bibliographic record
Abstract
Objective: This narrative review explores current research to broaden the understanding of dental hygienists' experiences and satisfaction in the workplace while suggesting strategies for the retention of dental hygienists and examining how current staffing shortages may impact the forthcoming Canadian Dental Care Plan. Methods: An electronic literature search was conducted in the following online databases: PubMed, Google Scholar, CINAHL, and Education Source. Only full-text, peer-reviewed articles written in English were selected for review. Results: The search yielded 18 articles with a wide range of study designs and methodologies. Discussion: Five key themes emerged from the literature: health impacts, psychosocial environment, strategies for retention, contradictory findings, and implications for the Canadian Dental Care Plan. Conclusion: Dental hygienists experience many health, environmental, and psychosocial challenges in their day-to-day working life that affect their job satisfaction and overall willingness to remain in the profession. Strategies for retention are essential for maintaining the current dental hygiene workforce and can have a positive impact on the Canadian Dental Care Plan and its success.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".