Management of Hospital Foundations: Does Compensation Matter?
Notice bibliographique
Résumé
[Abstract] Hospital foundations are run by boards and staff that may work as volunteers, or may be compensated. In addition to spending money on these, foundations often hire external fundraisers to oversee some major event such as a gala party or high-profile athletic event. This paper looks at these types of compensation, plus overall expenses and net assets, to build a regression model to forecast revenue of foundations. We find that expenses, assets, fundraising compensation and board compensation account for 80 percent of the variation in the amount of revenue generated by a foundation. [Keywords] Hospital foundations; board compensation; revenue Introduction All foundations are concerned about raising money to support their missions. But, in order to raise money, foundations usually spend money. Some foundations spend money on high-profile events such as gala parties or community 10-K runs. Some fundraise for a specific cause such as a new wing for their associated hospital. The occurrence of any of these events happens only with the support and hard work of people in the background: the board, the staff, and special events fundraisers. Some foundations rely exclusively on volunteer support and some foundations choose to compensate those involved. In addition to money spent on people, expenses might also include mailings, advertising, event spending, lawyers, accountants, investment managers and web-site specialists, to name a few. Income for a is limited to a few sources: investments and contributions. Foundation support has long been a mainstay of a hospital's search for funds (Morgan and Cohen, 1993). And as costs soar, support may become even more important in the future. Raymond (2005) found that even though health-related philanthropic contributions have doubled the last four decades, health care costs still exceed the rate of increase in giving related to health care. Aggarwal (2008) outlines the enormous projected increases in medical costs in the near-term, from 14.1% of GDP in 2003 to 17.7% by 2012. Given these projected future costs, foundations that support associated hospitals and their communities need to consider how to increase revenue. A study by Pink and Leatt (2006) looks at 80 foundations throughout Canada and found that one of the factors associated with increased revenue was a higher level of expenses. This paper looks at 178 foundations in the U.S. and analyzes areas where money is spent to see what has the greatest impact on revenue. More specifically, this study focuses on two variables common to all foundations, net assets and expenses, and three variables that occur in some, but not all, foundations in various combinations, compensation for board members, for staff, and for fundraising specialists. For these five variables, we investigate which are important for a good regression model to predict revenue, and of those useful for the regression model, which give the most return in revenue when increased. For the purposes of this research, the terms and hospital foundation describe a non-profit organization which devotes its efforts and resources to the support of a single hospital. All foundations researched are non-profit organizations, classified as 501(c)(3) and thus considered taxexempt by the federal government. These organizations are required to file a Form 990 annually to report their finances and revenue generating operations. These 990 filings were the basis for much of the data in this study. Data Set and Model Foundations with revenue less than 30 million were the focus of this study. There were 178 foundations in the sample representing hospitals of varying sizes (as measured by the number of beds) and throughout the United States. Initial listings of hospitals and foundations were located by using internet searches. …
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».