The Precepting Dilemma: A Reflexive Thematic Analysis Study of Midwifery Preceptors in Undergraduate Education in Canada
Bibliographic record
Abstract
Objective: To generate themes on facilitators and barriers for midwifery preceptorship.Methods: Midwifery preceptors in undergraduate education in Canada were invited to participate in one of three focus groups. A constructivist paradigm and reflexive thematic analysis approach was used for responses that were transcribed verbatim. Results: In September and October 2020, three focus groups took place comprising a total of 16 midwifery preceptors. Participants represented multiple Canadian jurisdictions and had a range of education, midwifery, and precepting experiences. Two primary themes, “the altruism of precepting” and “the lack of autonomy in precepting,” were generated from our analysis of participants’ responses. Preceptors also provided suggestions to better enable their role. Discussion/Conclusion: We interpreted a “precepting dilemma” in midwifery clinical teaching such that there are altruistic influences on the role and also an underlying lack of autonomy. Obligations and nurturing elements contributed to midwifery preceptors’ utilitarianism. Exacerbating a lack of sovereignty in precepting were deficits in collaboration and aspects that further marginalized the role. It is imperative for stakeholders of midwifery in Canada to carefully consider how altruism and autonomy of precepting affect the experiential curricula. This article has been peer reviewed.
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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 | 0.053 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.030 | 0.020 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".