Exploring dental hygiene decision making: A quantitative study of potential organizational explanations.
Bibliographic record
Abstract
BACKGROUND: To improve public access to oral health care, dental hygienists have been identified for practice expansion, and, therefore, they must demonstrate decision-making capacity. This study aimed to identify and test potentially influential factors in dental hygiene decision making. Organizational and gender factors were hypothesized to be most influential and focused the study. METHODS: A 2-phase mixed methods approach was used. In Phase I, a qualitative decision-making model was developed and subsequently published in 2012. Phase II tested aspects of the model through an electronic survey instrument and key informant interviews. This article reports on the statistical results of the quantitative survey. A third article will report on the qualitative thematic analyses and merged interpretation. RESULTS: The Phase I qualitative model guided the development of the survey instrument. The survey had a 38% response rate; moderate to weak correlations between predictor variables (structural and individual) and clinical decision making were shown. The final statistical model demonstrated that individual characteristics and graduating from a 3-year dental hygiene program were together significantly associated with decision-making capacity. DISCUSSION AND CONCLUSIONS: Individual characteristics and longer education were together shown to be associated with increased decision-making capacity. These findings did not show the organization or gender to be important in influencing decision-making capacity. However, the merging of the quantitative survey and qualitative key informant data will potentially inform how the organization influences the individual dental hygienist.
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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 | 0.019 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".