Interprofessional Teamwork: A Qualitative Study on Adapting Central Policies to Local Conditions in a Labour and Delivery Unit
Bibliographic record
Abstract
OBJECTIVES: We record the experiences of staff in a labour, delivery, and obstetric services (LD-OBS) unit in Alberta's largest quaternary medical centre-the Foothills Medical Centre (FMC)-as they navigated hospital policies during the COVID-19 pandemic. We examine how unit leadership applied these policies to better align with care delivery realities while staying true to the interprofessional nature of the unit. METHODS: A total of 12 semi-structured qualitative interviews were conducted with LD-OBS unit staff. Snowball and purposive sampling strategies were used to capture experiences from key informants. Interview transcripts underwent inductive coding. The themes identified through this process were discussed with members of the authorial team until a consensus was reached. RESULTS: FMC LD-OBS team members used 'interprofessional' as a value through which to interpret, adapt, and implement centrally developed COVID-19 policies. These were applied at 3 key moments: reconfiguring the unit, triaging, and rerouting patients, and contesting central personal protective equipment policies. LD-OBS leaders championed the importance of interprofessional collaboration and teamwork in the unit and worked to uphold it as a practice and value. CONCLUSION: The COVID-19 pandemic experience of the FMC LD-OBS unit illustrates the importance of considering interprofessionalism as a core value as policy was developed and implemented. Health authorities, hospitals, and other LD-OBS units may wish to consider how interprofessional work affects policy interpretation among health care teams, and how this may be leveraged to successfully adapt policies to local units, under both pandemic and 'normal' conditions.
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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".