More than Caring: Building a Foundation of Skills and Competencies to Teach Self-Advocacy in a Female-Dominated Profession
Bibliographic record
Abstract
External patriarchal interventions and market-driven forces have limited professional advancements and autonomy of the female-dominated dental hygiene (DH) profession since its foundation. The DH profession defines itself though caring and advocating for public access to its essential services. However, the lack of voice and autonomy are reported causes of DH practitioner burnout and labour shortages. DH education lacks training and foundational skills for self-advocacy. This Dissertation-in-Practice (DiP) explores the organizational context of a South-Western Ontario DH college (SWO College or the school) and addresses a complex problem of practice (PoP) that recognizes the barriers, both past and present, that kept DH educators from teaching self-advocacy and recognizes the needs of these educators to develop the required skills and competencies. As change agent, my agency as a female DH educator supports a feminist lens to create novel ideas and solutions. A framework analysis of sociocultural recognition, political representation, and technical/economical distribution paradigms reveals rigid hierarchy and gender inequality in DH education. An envisioned future for SWO College supports collaborative bottom-up community of practice peer mentoring led by ethical and transformational leadership approaches. The ADKAR change model fosters an individualized focus on DH educators’ needs to acquire the knowledge, communication, and leadership components of self-advocacy. The PDSA cycle monitors and evaluates each change step. The DiP concludes with next steps and future considerations for the school.
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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 | 0.006 | 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 | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".