Hvilke personlige og faglige egenskaper er prediktive drivere til salgsprestasjoner for en Key Account Manager?

I innledningen av oppgaven blir problemstillingen presentert sammen med oppgavens avgrensninger, og hvordan oppgavens struktur er bygget opp. I det andre kapittelet i oppgaven blir det teoretiske grunnlaget presentert. Denne oppgaven har tatt utgangspunkt i hvordan en key account manager sine prestasjoner påvirkes, og vi har på bakgrunn av dette gjort rede for teori rundt key account management sammen med salgsprestasjonsmodellen til Johnston og Marshall. Salgsprestasjonsmodellen til Johnston og Marshall er hovedmodellen som denne oppgaven er bygget på. De relevante faktorene i modellen er gjort rede for siden de har en påvirkning på prestasjon. Det tredje kapitelet i oppgaven tar for seg valgene som er tatt i forhold til det metodiske fundamentet i undersøkelsen, og grunnlaget for datainnsamling og videre drøfting av problemstilling. Det er gjennomført dybdeintervju med seks salgsledere med rekruteringsansvar for key account managere, som har vært grunnlaget for primærdata. Informasjonen som ble tilegnet her, ble videre brukt til å utarbeide en intervjuguide, som videre la grunnlaget for analysen av problemstillingen. I det fjerde kapitelet analyseres og drøftes empirifunn som ble gjort under dybdeintervjuene opp imot teorigrunnlaget i oppgaven. Vi søker å finne forståelse rundt hvilke drivere som er prediktive for salgsprestasjoner og hvilke forskjeller det finnes mellom teori og hva som praktiseres i arbeidslivet. Funnene i analysen vil gi premisser for konklusjon i oppgaven, men ikke generalisering. Siste del av oppgaven inneholder refleksjoner rundt egen oppgave, oppgavens konklusjon og forslag til videre forskning

Først og fremst. Behovsdrevet og brukerstyrt digital læringsteknologi for førstehjelp

Først og fremst var et innovasjonsprosjekt med mål om å utvikle og ta i bruk en behovsdrevet og brukerstyrt digital læringsteknologi for å øke digital kompetanse i den norske skolen. Teknologien fra prosjektet skulle også forsterke den nasjonale satsningen "Sammen redder vi liv" og temaet livreddende førstehjelp i grunnskolen, og være en digital læringsressurs for temaet livsmestring og helse i ny læreplan. Resultatet fra prosjektet ble en digital plattform hvor man kan skape og lagredigitale læringsressurser som kan oppleves i VR. I tillegg ble det utviklet en spesifikk læringsressurs for VR-briller om risikovurdering, som er et viktig tema innenfor førstehjelp. Læringsressursen ble utviklet gjennom samskaping, hvor elever og lærere har deltatt som medprodusenter og skuespillere. Rapporten beskriver utviklingen i prosjektet, trekker opp de viktigste forskningsresultatene og presenterer en del momenter fra våre erfaringer som vi tenker andre kan lære av.Først og fremst. Behovsdrevet og brukerstyrt digital læringsteknologi for førstehjelppublishedVersio

Assessing the resilience of sustainable autonomous shipping:New methodology, challenges, opportunities

This paper introduces a resilience assessment methodology for sustainable autonomous maritime transport networks developed by the European project entitled “Advanced, Efficient, and Green Intermodal Systems” (AEGIS). This problem being addressed in this paper concerns the investigation of threats, incidents, and risks in an autonomous- and sustainable shipping context, and the research question is the development of both preventive measures and reactive actions to maintain an acceptable level of operational constraints. The paper's methodology aids in designing sustainable logistics systems for highly automated waterborne transport, identifying threats and barriers to mitigate event consequences, thereby facilitating a seamless green transition. To examine the usability, this methodology is applied in a case study for cargo transportation, where we in this paper consider the maritime corridor between Trondheim and Rotterdam. The findings encompass the spectrum of possible actions to prevent and mitigate unwanted events and enhance resilience and flexibility. This can be used as a tool to respond to unwanted threats, enhance safety, and introduce new strategies. These results are deemed important as resilience is one of the prerequisites for the development of a sustainable transport system. This is true both for the companies that are engaged in the operation of such systems and for policymakers.</p

Skåringsverktøy for pasienter med lungeemboli for hjemmebehandling

Denne oppgaven tar for seg problematikken rundt hjemmebehandling av pasienter med lungeemboli. Her diskuterer vi de mest sentrale publikasjonene, inkludert primærstudiene for Hestia og PESI/sPESI. Vi konkluderer med at både Hestia og sPESI er godt validerte og anvendelige verktøy for risikovurdering av pasienter med erkjent lungeemboli. Likevel konkluderer vi med at det for vårt sykehus ikke er aktuelt med innføring av tiltaket, som diskutert i teksten

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

Importance Early identification of cerebral palsy (CP) is important for early intervention, yet expert-based assessments do not permit widespread use, and conventional machine learning alternatives lack validity. Objective To develop and assess the external validity of a novel deep learning–based method to predict CP based on videos of infants’ spontaneous movements at 9 to 18 weeks’ corrected age. Design, Setting, and Participants This prognostic study of a deep learning–based method to predict CP at a corrected age of 12 to 89 months involved 557 infants with a high risk of perinatal brain injury who were enrolled in previous studies conducted at 13 hospitals in Belgium, India, Norway, and the US between September 10, 2001, and October 25, 2018. Analysis was performed between February 11, 2020, and September 23, 2021. Included infants had available video recorded during the fidgety movement period from 9 to 18 weeks’ corrected age, available classifications of fidgety movements ascertained by the general movement assessment (GMA) tool, and available data on CP status at 12 months’ corrected age or older. A total of 418 infants (75.0%) were randomly assigned to the model development (training and internal validation) sample, and 139 (25.0%) were randomly assigned to the external validation sample (1 test set). Exposure Video recording of spontaneous movements. Main Outcomes and Measures The primary outcome was prediction of CP. Deep learning–based prediction of CP was performed automatically from a single video. Secondary outcomes included prediction of associated functional level and CP subtype. Sensitivity, specificity, positive and negative predictive values, and accuracy were assessed. Results Among 557 infants (310 [55.7%] male), the median (IQR) corrected age was 12 (11-13) weeks at assessment, and 84 infants (15.1%) were diagnosed with CP at a mean (SD) age of 3.4 (1.7) years. Data on race and ethnicity were not reported because previous studies (from which the infant samples were derived) used different study protocols with inconsistent collection of these data. On external validation, the deep learning–based CP prediction method had sensitivity of 71.4% (95% CI, 47.8%-88.7%), specificity of 94.1% (95% CI, 88.2%-97.6%), positive predictive value of 68.2% (95% CI, 45.1%-86.1%), and negative predictive value of 94.9% (95% CI, 89.2%-98.1%). In comparison, the GMA tool had sensitivity of 70.0% (95% CI, 45.7%-88.1%), specificity of 88.7% (95% CI, 81.5%-93.8%), positive predictive value of 51.9% (95% CI, 32.0%-71.3%), and negative predictive value of 94.4% (95% CI, 88.3%-97.9%). The deep learning method achieved higher accuracy than the conventional machine learning method (90.6% [95% CI, 84.5%-94.9%] vs 72.7% [95% CI, 64.5%-79.9%]; P < .001), but no significant improvement in accuracy was observed compared with the GMA tool (85.9%; 95% CI, 78.9%-91.3%; P = .11). The deep learning prediction model had higher sensitivity among infants with nonambulatory CP (100%; 95% CI, 63.1%-100%) vs ambulatory CP (58.3%; 95% CI, 27.7%-84.8%; P = .02) and spastic bilateral CP (92.3%; 95% CI, 64.0%-99.8%) vs spastic unilateral CP (42.9%; 95% CI, 9.9%-81.6%; P < .001). Conclusions and Relevance In this prognostic study, a deep learning–based method for predicting CP at 9 to 18 weeks’ corrected age had predictive accuracy on external validation, which suggests possible avenues for using deep learning–based software to provide objective early detection of CP in clinical settings

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy from Spontaneous Movements in Infants at High Risk

Importance: Early identification of cerebral palsy (CP) is important for early intervention, yet expert-based assessments do not permit widespread use, and conventional machine learning alternatives lack validity. Objective: To develop and assess the external validity of a novel deep learning-based method to predict CP based on videos of infants' spontaneous movements at 9 to 18 weeks' corrected age.Design, Setting, and Participants: This prognostic study of a deep learning-based method to predict CP at a corrected age of 12 to 89 months involved 557 infants with a high risk of perinatal brain injury who were enrolled in previous studies conducted at 13 hospitals in Belgium, India, Norway, and the US between September 10, 2001, and October 25, 2018. Analysis was performed between February 11, 2020, and September 23, 2021. Included infants had available video recorded during the fidgety movement period from 9 to 18 weeks' corrected age, available classifications of fidgety movements ascertained by the general movement assessment (GMA) tool, and available data on CP status at 12 months' corrected age or older. A total of 418 infants (75.0%) were randomly assigned to the model development (training and internal validation) sample, and 139 (25.0%) were randomly assigned to the external validation sample (1 test set). Exposure: Video recording of spontaneous movements.Main Outcomes and Measures: The primary outcome was prediction of CP. Deep learning-based prediction of CP was performed automatically from a single video. Secondary outcomes included prediction of associated functional level and CP subtype. Sensitivity, specificity, positive and negative predictive values, and accuracy were assessed.Results: Among 557 infants (310 [55.7%] male), the median (IQR) corrected age was 12 (11-13) weeks at assessment, and 84 infants (15.1%) were diagnosed with CP at a mean (SD) age of 3.4 (1.7) years. Data on race and ethnicity were not reported because previous studies (from which the infant samples were derived) used different study protocols with inconsistent collection of these data. On external validation, the deep learning-based CP prediction method had sensitivity of 71.4% (95% CI, 47.8%-88.7%), specificity of 94.1% (95% CI, 88.2%-97.6%), positive predictive value of 68.2% (95% CI, 45.1%-86.1%), and negative predictive value of 94.9% (95% CI, 89.2%-98.1%). In comparison, the GMA tool had sensitivity of 70.0% (95% CI, 45.7%-88.1%), specificity of 88.7% (95% CI, 81.5%-93.8%), positive predictive value of 51.9% (95% CI, 32.0%-71.3%), and negative predictive value of 94.4% (95% CI, 88.3%-97.9%). The deep learning method achieved higher accuracy than the conventional machine learning method (90.6% [95% CI, 84.5%-94.9%] vs 72.7% [95% CI, 64.5%-79.9%]; P Conclusions and Relevance: In this prognostic study, a deep learning-based method for predicting CP at 9 to 18 weeks' corrected age had predictive accuracy on external validation, which suggests possible avenues for using deep learning-based software to provide objective early detection of CP in clinical settings.</p