Evaluation in Our New Normal Environment: Navigating the Challenges with Data Collection
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Notice bibliographique
Résumé
Background: Data collection is a critical component of all evaluations. However, it often presents a number of challenges under the best of circumstances. For instance, the evaluation budget and time frame both have implications for the quality and type of data that is collected. Additionally, adherence to high quality international ethical best practices is necessary when collecting data for any purpose, methodological rigor is important for ensuring the credibility of the evaluation, improving access to important documents and stakeholders, as well as decreasing excessive evaluation anxiety on the part of critical stakeholders, when possible, is vital. These challenges have now been considerably exacerbated by the COVID-19 global health pandemic which has changed our world in fundamental ways. In what is now considered as our new normal environment, evaluators will need to make profound changes to the manner in which they plan and undertake data collection. Objectives: This paper examines the many and varied challenges that will be encountered with data collection in our new normal environment. This new normal has had an impact on evaluation practices in all countries, developed and developing, and has significantly amplified existing challenges in countries with limited evaluation culture, budgets, technological coverage, access, and connectivity. It makes an important contribution to the literature since data collection has historically and traditionally been conducted using primarily face-to-face field work and through the freedom of movement of people to undertake this task. Setting: Not applicable. Intervention: Not applicable. Research Design: Desk review was utilized for the preparation of this paper. Findings: Evaluators need to be extremely flexible, innovative, and amendable to different approaches to data collections as our new normal environment will likely be with us for a while. This pandemic has thrown everyone a very painful curveball and introduced significant new work-related challenges for a myriad of work types and work environments. Innovation and the willingness to learn new methods have become an important necessity to help with learning, accountability, transparency. The COVID-19 pandemic has highlighted the plight of the most vulnerable and evidence-based data is the only means to assist this group. Evaluators must rise to the challenge, devise new ways to collect data that is credible and useful, and continue to promote the importance and benefits of the field of evaluation. As such, evaluators have an important role to play in the global economic recovery efforts.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,057 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 découle