Nurses’ critical event risk assessments: a judgement analysis
Notice bibliographique
Résumé
AIMS: To explore and explain nurses' use of readily available clinical information when deciding whether a patient is at risk of a critical event. BACKGROUND: Half of inpatients who suffer a cardiac arrest have documented but unacted upon clinical signs of deterioration in the 24 hours prior to the event. Nurses appear to be both misinterpreting and mismanaging the nursing-knowledge 'basics' such as heart rate, respiratory rate and oxygenation. Whilst many medical interventions originate from nurses, up to 26% of nurses' responses to abnormal signs result in delays of between one and three hours. METHODS: A double system judgement analysis using Brunswik's lens model of cognition was undertaken with 245 Dutch, UK, Canadian and Australian acute care nurses. Nurses were asked to judge the likelihood of a critical event, 'at-risk' status, and whether they would intervene in response to 50 computer-presented clinical scenarios in which data on heart rate, systolic blood pressure, urine output, oxygen saturation, conscious level and oxygenation support were varied. Nurses were also presented with a protocol recommendation and also placed under time pressure for some of the scenarios. The ecological criterion was the predicted level of risk from the Modified Early Warning Score assessments of 232 UK acute care inpatients. RESULTS: Despite receiving identical information, nurses varied considerably in their risk assessments. The differences can be partly explained by variability in weightings given to information. Time and protocol recommendations were given more weighting than clinical information for key dichotomous choices such as classifying a patient as 'at risk' and deciding to intervene. Nurses' weighting of cues did not mirror the same information's contribution to risk in real patients. Nurses synthesized information in non-linear ways that contributed little to decisional accuracy. The low-moderate achievement (R(a)) statistics suggests that nurses' assessments of risk were largely inaccurate; these assessments were applied consistently among 'patients' (scenarios). Critical care experience was statistically associated with estimates of risk, but not with the decision to intervene. CONCLUSION: Nurses overestimated the risk and the need to intervene in simulated paper patients at risk of a critical event. This average response masked considerable variation in risk predictions, the need for action and the weighting afforded to the information they had available to them. Nurses did not make use of the linear reasoning required for accurate risk predictions in this task. They also failed to employ any unique knowledge that could be shown to make them more accurate. The influence of time pressure and protocol recommendations depended on the kind of judgement faced suggesting then that knowing more about the types of decisions nurses face may influence information use. RELEVANCE TO CLINICAL PRACTICE: Practice developers and educators need to pay attention to the quality of nurses' clinical experience as well as the quantity when developing judgement expertise in nurses. Intuitive unaided decision making in the assessment of risk may not be as accurate as supported decision making. Practice developers and educators should consider teaching nurses normative rules for revising probabilities (even subjective ones) such as Bayes' rule for diagnostic or assessment judgements and also that linear ways of thinking, in which decision support may help, may be useful for many choices that nurses face. Nursing needs to separate the rhetoric of 'holism' and 'expertise' from the science of predictive validity, accuracy and competence in judgement and decision making.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,069 | 0,302 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,009 | 0,007 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».