Skåringsverktøy som kartlegger sykepleie på nyfødtintensiv - en litteraturstudie
Bibliographic record
Abstract
Hensikt: I norske nyfødtintensiv avdelinger er det anbefalt bruk av skåringsverktøy for å kartlegge sykepleie. Dagens praksis innebærer at pasienter som er innlagt på nyfødtintensiv avdelinger kategoriseres etter medisinske kriterier, og ikke ut fra behov for sykepleie. I tillegg bygger dagens retningslinjer for kvalitet og kompetanse på nyfødtintensiv på et litteratur- og forskningsgrunnlag publisert før 2017. Hensikten med litteraturstudien er å utforske hvilke skåringsverktøy som benyttes for å kartlegge sykepleie på nyfødtintensiv avdelinger.\n\nMetode: En litteraturstudie, hvor trinnene til en scoping review er anvendt. Et systematisk søk etter nordisk- eller engelskspråklige studier fra 2008 til 2023 ble utført i tre elektroniske databaser: PubMed, CINAHL og Oria. Inklusjonskriteriene var skåringsverktøy, sykepleie eller intensivsykepleie og nyfødtintensiv avdelinger.\n\nResultat: Åtte artikler fra Brasil, USA og Canada, og sju skåringsverktøy ble inkludert. Ingen skåringsverktøy fra Norge eller Europa ble identifisert. Tre overordnede temaer for innhold i skåringsverktøyene ble identifisert: klinisk-, administrativ- og familiesentrert sykepleie. Det fantes en variasjon i hvordan verktøyene ble brukt i praksis, men ble overordnet benyttet til nivåinndeling av pasientene.\n\nKonklusjon: Det benyttes sju skåringsverktøy som kartlegger sykepleie på nyfødintensiv avdelinger. Resultatet viste store variasjoner i skåringsverktøyenes innhold og kvalitet. Det er behov for mer forskning med fokus på å utvikle, validere eller implementere et skåringsverktøy som kartlegger sykepleie på norske nyfødtintensiv avdelinger.\n\n\nAim: It is recommended in Norwegian Neonatal intensive care units to use an assessment tool for mapping of nursing care. Current practices involve categorizing patients in neonatal intensive care units based on medical criteria rather than nursing care needs. The existing guidelines for quality and competence in neonatal intensive care are built on literature and research published before 2017. The purpose with the literature study is to explore which assessment tools are being used for mapping nursing care in the neonatal intensive care units.\n \nMethod: A literature study, utilizing the steps of a scoping review. A systematic search for Nordic- or English-language studies from 2008 to 2023 was conducted in three electronic databases: PubMed, CINAHL and Oria. Inclusion criteria were assessment tools, nursing or intensive nursing care, and neonatal intensive care units.\n\nResults: Eight studies from Brazil, USA and Canada, and seven assessment tools were included. No assessment tools from Norway or Europe were identified. Three main themes for the content of the assessment tools were identified: clinical, administrative- and family centered nursing care. There was variation in how the tools were used in practice, but it was mainly used to classify the patients into levels.\n \nConclusion: Seven assessment tools are used to map nursing care in neonatal intensive care units. The results showed significant variations in the content and quality of the assessment tools. There is a need for more research on developing, validating or implementing assessment tools for mapping nursing care in Norwegian neonatal intensive care units.
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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 | 0.047 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.012 |
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".