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Enregistrement W4287527612 · doi:10.4103/kjo.kjo_194_21

Connect, collaborate, contribute, and create

2021· article· en· W4287527612 sur OpenAlexaboutno aff
V Sudha

Notice bibliographique

RevueKerala Journal of Ophthalmology · 2021
Typearticle
Langueen
DomaineHealth Professions
ThématiqueGlobal Healthcare and Medical Tourism
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTeamworkPublic relationsPopularityWorkforceAction (physics)Health careWork (physics)PsychologyProduct (mathematics)Medical educationBusinessMedicinePolitical scienceEngineeringSocial psychology

Résumé

récupéré en direct d'OpenAlex

“No one is big enough to be independent of others.” -Will Mayo As this Editorial team reaches the end of its 2 year tenure, I wish to thank each and every member of the team for the excellent teamwork and cooperation I received during this time. Selecting articles, reviewing, writing up sections, proofreading, and encouraging others to contribute, were all done in a timely and effective manner. Hopefully, we have together been able to raise the standards of our Journal. The encouragement and effective suggestions I received from my senior colleagues, timely reviews by expert reviewers from KSOS and outside, played a very important role in keeping the momentum going. In my final editorial, I would like to highlight this extremely important concept of Teamwork and Collaboration. The concept of Networking for individual professional upliftment is gaining popularity. However, if we can cooperate with each other for the benefit of Ophthalmology as a whole, and patient treatment patterns, in particular, we reach nearer to our shared goal of reducing visual disability. EVERYBODY WINS The World Health Organization (WHO) recognizes that collaborative practice strengthens health systems and improves health outcomes, and is an innovative strategy that will play an important role in mitigating the global health workforce crisis. The Framework for Action on Interprofessional Education and Collaborative Practice is the product of the WHO Study Group on this practice.[1] Collaboration is evident when health-care professionals communicate with each other, assume complementary roles to cooperatively work together, and share responsibility for problem-solving and decision-making. Specific collaborative activities include sharing of information, discussion of complicated cases, and referrals to colleagues. The developing concept of Group Practice and its advantages has been highlighted by the President, KSOS, in the following pages of this journal issue. COLLABORATIONS IN OPHTHALMOLOGY Being able to take care of the sight of a patient is both a responsibility and a privilege. Because this is our ultimate goal: to eliminate blindness and visual impairement. Many useful examples of cooperation have been seen in Ophthalmology. Research Collaboration is vital in driving forward research and innovation seeking to develop solutions to refine the diagnosis, management, and treatment of patients with eye diseases. There is a well-identified need for patient-oriented clinical research and this can only be achieved by creating active collaboration between academic centers with competence for clinical research, with support by an infrastructure that provides appropriate management of clinical trials at a realistic cost. In Ophthalmology, the example was set by the Diabetic Retinopathy Clinical Research (DRCR. net) Retina Network in the USA, formed in 2002 through a National Eye Institute and National Institute of Diabetes and Digestive and Kidney Diseases-sponsored cooperative agreement. The objective was to develop a collaborative network to facilitate multicenter clinical research on Diabetic Retinopathy and Diabetic Macular Edema, and has now been expanded to include research on other retinal diseases. It used the combined strengths of academic and community retina sites in the infrastructure as well as created opportunities for industry collaboration while maintaining rigorous academic independence from pharmaceutical interests.[2] Since 2002, the DRCR. net has initiated and completed numerous multicenter studies in DR with over 160 participating sites and 500 physicians throughout the United States and Canada. Multicenter Data Retrieval Data from various sources can be integrated into a common registry and provide important information. Useful applications can be derived too from these. Data derived from the Sight Outcomes Research Collaborative Ophthalmology Data Repository, which captures electronic health record data of all patients receiving any eye care at academic medical centers, was used for developing an algorithm useful in triaging patients in the COVID pandemic based on glaucoma severity and progression risk by identifying patients whose appointments could safely get postponed and facilitated prioritization of appointments for rescheduling.[3] Clinical Practice Guidelines We are beginning to work together to develop consensus statements about eye care and train ourselves on how to use best practice guidelines. An international, expert-led consensus initiative was set up by the Collaborative Ocular Tuberculosis Study group to develop systematic, evidence, and experience-based recommendations for the treatment of ocular TB using a modified Delphi technique process.[4] Sharing Knowledge And Expertise Improving education and training to raise standards in Ophthalmology worldwide[56] is being followed by many important organizations, and is best exemplified by the online academic resource, EyeWiki, which is a collaboration between the American Academy of Ophthalmology and multiple societies.[7] Networking for Professional Career Advancement There is a critical need to help ophthalmologists maintain their competency and learn new skills, forging valuable relationships with peers. Organized mentorship programs can play a key role in fostering the development of careers in ophthalmology.[8] Stronger personal relationships can nurture innovative strategies with colleagues, engage new energy, and maintain momentum when obstacles seem overwhelming. Public Education Public–private partnerships can improve population health by advancing public health strategies and policies, improving public health education and advocacy, fostering trust and collaboration among sectors and stakeholders, and improving access to health care.[9] Newer Technologies A universal artificial intelligence (AI) platform developed for collaborative management of cataracts involving multilevel clinical scenarios explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage. It showed robust diagnostic performance and effective service for cataracts.[10] Learning how to work in teams, brainstorming on issues, and networking with the right people can improve our individual practices. Publications based on multicentric research can provide data on comprehensive real-life effectiveness of various treatment strategies, especially in resource-poor regions where implementing strict guidelines may not be feasible. Innovations like using AI in Ophthalmology are made possible through collaboration among scientists, medical professionals, and technological experts. Thus, this concept needs to be nurtured and encouraged by all professional societies. Clarity and transparency in the collaboratorship process are essential in nurturing these networks and carrying them forward in future practice. Signing off with best wishes to the incoming Editorial team ……

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,044
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,056
Tête enseignante GPT0,445
Écart entre enseignants0,389 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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