Application of the Dental Hygiene Human Needs Conceptual Model and the Oral Health‐Related Quality of Life Model to the dental hygiene curriculum in Japan
Bibliographic record
Abstract
This paper reports the incorporation of the Dental Hygiene Human Needs Conceptual Model (DHHN) and the Oral Health-Related Quality of Life Model (OHRQL) into a dental hygiene curriculum in Japan. A simulated patient practice was offered to 67 dental hygiene students. In the practice activity, all students were introduced to the use of an OHRQL assessment tool. A DHHN assessment tool was utilized additionally only by the experimental student group. The statistical analysis of the post-practice survey showed that the OHRQL instrument was more helpful in assessment and problem identification than the DHHN instrument. By contrast, text-based analysis of dental hygiene diagnostic statements showed that the experimental group identified more domains of patients' human needs deficits than the control group. This suggested the possibility that the DHHN model helped them to see patients from broader perspectives. However, it was difficult for students to design care plans attending to the domains of the models. Also, in considerations to the cultural issues, the validity and equivalence of the Japanese versions of both models should be further investigated. Within the limitation of the present study, the results suggested that incorporation of the combination of the DHHN and OHRQL models can be useful in a dental hygiene curriculum, as each tool helps students expand the perspective from which they view client. Further improvements in learning strategies should facilitate the effective utilization of these models.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".