Perceptions on Use of the Subjective Global Assessment Before and After It Became Part of Regular Practice
Bibliographic record
Abstract
BACKGROUND: Subjective global assessment (SGA) is a standardized diagnostic tool for malnutrition and identifies those who would benefit from nutrition treatment. SGA has been validated in several patient populations; however, implementation in clinical practice is inconsistent. The objective of this study was to understand and contrast the perceptions on use of SGA before and after it became a regular practice for hospital dietitians. METHODS: The More-2-Eat implementation project provided the opportunity to undertake this qualitative study, as 5 hospitals adopted INPAC (Integrated Nutrition Pathway for Acute Care), which includes the use of SGA. Between 2015 and 2018, interviews were conducted with dietitians at baseline (n = 9), a year after implementation (n = 18), and a year after project completion (n = 6). Thematic analysis was conducted. RESULTS: Themes before SGA adoption included a desire for a malnutrition diagnosis and care planning; lacking comfort in use of SGA; and reflecting on SGA training needs. After a year of implementing INPAC and a year after project completion, themes described becoming a better clinician; allowing dietitians to see the right people, sooner; recognizing the variability in the treatment path for mildly/moderately malnourished (SGA B) patients; improving overall efficiency in delivery of care; and establishing policy and procedures to sustain and spread use of SGA. CONCLUSION: Initially, dietitians were hesitant to use SGA. Adoption through a focused implementation study and experience with implementation of SGA changed these perceptions. Understanding these perceptions before and after use may support adoption of this useful diagnostic tool.
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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".