22 Neonatal regional outreach education in Quebec – a formal needs assessment
Bibliographic record
Abstract
Abstract Background In Montreal (Quebec, Canada), newborn care is provided in either highly specialized hospitals for complex/critically ill patients or community hospitals for healthier patients. Community health care practitioners (CHP) have varying levels of comfort and competency in newborn care. Also, because of the unpredictable nature of obstetrics, CHP will need to manage critically ill newborns throughout their careers. Such high acuity, low frequency events can be difficult to manage even in experienced hands. Therefore, there is an imperative to develop a neonatal training curriculum for CHP, with an emphasis on critical newborns. Objectives As a first step, the objective of this study was to perform a comprehensive needs assessment of neonatal outreach education for CHP working in the Greater Montreal Area (Quebec, Canada). Design/Methods Using a mixed methods design (figure), the needs assessment was divided in 3 sections. First, the felt educational needs of the target population (CHP) using an online questionnaire. Second, through focus group discussion, the normative needs of the medical personnel who transport critical newborns. Third, the expressed educational needs of CHP through analysis of neonatal transport data between 2017-2020. Quantitative data was analyzed using descriptive statistics, including means and medians. Qualitative data was coded using conventional content analysis, with the primary technique being descriptive and pattern coding. Results The questionnaire reached 158 participants across 13 community hospitals. CHP are seeking training with procedures, critical situations, and resuscitation. Also, resource sharing, more field expertise, and additional resources for training (time, credits) were identified. Three focus groups (15 participants) also revealed that procedural training, critical newborn management and ventilation were important, as well as Crisis Resource Management (CRM) training. Both the questionnaire and focus groups identified specific medical conditions of interest. The expressed needs (transport data, 947 transports) helped reinforce felt and normative needs. Simulation was identified as a key educational technique. Conclusion A neonatal educational outreach curriculum will need to include procedural training, CRM teaching and certain specific medical conditions including extreme prematurity, hypoxic-ischemic encephalopathy (HIE) and pneumothorax. Procedural training and the principles of ventilation are also critically important. Simulation should be integral, in situ and with varied content. The fear of being judged is omnipresent and should be recognized during outreach education. Educational resources should be shared on an easily accessible, central website.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".