Needs assessment for interprofessional education module on prevention and early detection of oral cancer among dental interns: a cross- sectional survey
Bibliographic record
Abstract
BACKGROUND: The challenges associated with ensuring widespread system changes to enable early diagnosis and prevention of oral cancer could benefit from interprofessional practice. A needs assessment study was conducted to inform the Interprofessional Education and Collaborative Practice (IPECP) course related to oral cancer. The primary objectives of this study were 1) to establish a tool assess the knowledge attitude and practice (KAP) related to prevention and early detection of oral cancer of health professional students, and 2) to assess the same KAP of pre-licensure dental students. Additional objectives were to consider the possibility that dental students would demonstrate good scores related to early detection and prevention of oral cancer thus indicating their readiness for interprofessional learning and collaborative practice. METHODS: Two questionnaires were utilized for this study which included 1) Readiness for interprofessional learning was assessed using the pre- validated tool of Readiness for Interprofessional Learning Scale (RIPLS) 2) A questionnaire to assess the KAP related to early diagnosis and prevention of oral cancer which was developed, validated, and evaluated. Statistical analysis includes, descriptive statistics, Mann-Whitney U test, Ordered logistic regression and Probit analysis. p value was set at < 0.05. RESULTS: A total of 130 dental students (74.6% female) were included in the study. Mean scores related to KAP were 15.96 ± 1.394, 4.70 + 1.146, 7.02 ± 1.019 respectively. The mean score of RIPLS was 73.15 ± 15.961. The probability of overall samples to have good RIPLS scores was around 0.68 to 0.76 (Male 0.68-0.82 & Female 0.68 -0.74). The percentage of students having good knowledge score was 93.8%, good attitude score was around 54.6% and good practice score was around 90%. CONCLUSION: Knowledge and practice related to prevention and early detection of oral cancer were scored highly. Attitude scores were lower in a relatively higher proportion of participants and needed to be addressed in the curriculum. RIPLS score indicates a positive attitude towards interprofessional learning.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".