Stillbirth inequalities among American Indians and Alaska Natives, 2003-2012
Bibliographic record
Abstract
country programs.USAMC staff with necessary expertise and experience conducted the project.High-level SSAAMC assessments were carried out followed by the selection of two sites for two 10-day surgical care, teaching and capacity building pilots.In addition to utilizing standard data collection tools, the design of the assessment itself was a means to gather additional data relevant to assessing capacity building objectives.To collaboratively test surgical feasibility, USAMC faculty and trainees worked shoulder-to-shoulder with SSAAMC staff to triage patients, conduct surgeries, provide postoperative care, and establish treatment plans.Additionally, the USAMC team led didactic presentations and participated in surgical rounds.SSAAMC and USAMC leadership evaluated relative value of a partnership and subsequently developed long-term, shared program goals assuring program ownership by all parties.Outcomes & Evaluation: During the project year, 45 SSAAMC faculty and trainees participated in capacity building activities, 42 surgical training cases were conducted, 10 USAMC health professionals gained global experiences, and a long-term institutional relationship was established.Going Forward: Challenges include: faculty and trainees at SSAAMC and USAMC lack dedicated time to participate in program activities; alignment and coordination of several local and international stakeholders; supply chain and equipment needs unique to care of pediatric.Funding: USAAMC provided direct and in-kind funding for the project; SSAAMCs provided in-kind
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".