Assessing Mitral Valve Stenosis by Real-time 3-Dimensional Echocardiography in Systemic Lupus Erythematosus: A Look Inside the Heart: Figure 1.
Bibliographic record
Abstract
Background Māori regard stories as a preferred method for imparting knowledge through waiata (song), moteatea (poetry), kauwhau (moralistic tale), pakiwaitara (story) and purakau (myths). Storytelling is also an expression of tinorangatiratanga (self-determination); Māori have the right to manage their knowledge, which includes embodiment in forms transcending typical western formulations. Digital storytelling is a process by which ‘ordinary people’ create short autobiographical videos. It has found application in numerous disciplines including public health and has been used to articulatethe experiences of those often excluded from knowledge production. Aim To explore the use of digital storytelling as a research method for learning about whānau (family) experiences providing end of life care for kaumātua (older people). Methods Eight Māori and their nominated co-creators attended a three-day digital story telling workshop led by co-researchers Shuchi Kothari and Sarina Pearson. They were guided in the creation of first-person digital stories about caring for kaumātua. The videos were shared at a group screening, and participants completed questionnaires about the workshop and their videos. A Kaupapa Māori narrative analysis was applied to their stories to gain new perspectives on Māori end of life caregiving practices. (Kaupapa Maori research privileges Maori worldviews and indigenous knowledge systems.) Results Digital storytelling is an appropriate method as Māori is an oral/aural society. It allows Māori to share their stories with others, thus promoting community support at the end of life, befitting a public health approach. Conclusion Digital storytelling can be a useful method for Māori to express their experiences providing end of life caregiving.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".