Multidisciplinary Collaborative Maternity Care in Canada: Easier Said Than Done
Bibliographic record
Abstract
OBJECTIVE: To describe care provider attitudes towards multidisciplinary collaborative maternity care in Canada and the factors influencing such care from the perspective of members of national professional associations of care providers. METHODS: A qualitative descriptive approach was used. Leaders of national associations nominated key members, who were invited to participate in semi-structured telephone interviews. RESULTS: Twenty-five participants from six national care provider associations (family physicians, obstetricians, registered midwives, registered nurses, nurse practitioners, and rural physicians) were interviewed. Participants described at least one of two main benefits of collaborative maternity care: a partial solution to the human resources shortage in maternity care, and improved maternity care for women. Despite their belief that collaboration is needed, participants expressed concern about the effects of collaboration on their practice. In particular, some participants were concerned about how collaborative models could support woman-centred care or respond to local community needs and promote continuity of care. Significant barriers to collaboration include structural factors (fee structure, liability issues) and interdisciplinary rivalry between groups of providers (turf protection, lack of mutual respect). Strategies to promote collaboration that were supported by the participants include strong national leadership and interdisciplinary education. CONCLUSION: Representatives of professional associations of care providers believe that multidisciplinary collaborative maternity care is needed to sustain the availability of care providers and to improve access and women's choices for maternity care in Canada. However, they perceive that strong leadership and education are needed to address significant structural and relational barriers to collaborative practice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".