Medical Education Progress Report: Section B: Phase I Elements - Planning & Development

Outline of the LCME 3-stage Accreditation Process for Medical Education. Details the stages in the College of Medicine's efforts to initiate the accreditation process. Also details the College of Medicine's Implementation Plan and Proposed Timeline for implementation.

Medical Education Progress Report (Updates)

Documents detailing the LCME accreditation process, and the proposed outline for implementation of the FLorida International University College of Medicine.

Progress Report, February 1- March 10, 2007

Timeline/progress report outlining meetings concerning the College of Medicine planning process.

Personal autonomy in discharge planning decision -making in mentally alert hospitalized elderly individuals: The social workers' perspective

This study was conducted to understand (a) hospital social workers' perspectives about patients' personal autonomy and self-determination, (b) their experiences, and (c) their beliefs and behaviors. The study used the maximum variation sampling strategy to select hospitals and hospital social work respondents. Individual interviews were conducted with 31 medical/surgical and mental health hospital social workers who worked in 13 hospitals. The data suggest the following four points. First, the hospital setting as an outside influence as it relates to illness and safety, and its four categories, mentally alert patients, family members, health care professionals, and social work respondents, seems to enhance or diminish patients' autonomy in discharge planning decision making. Second, respondents report they believe patients must be safe both inside and outside the hospital. In theory, respondents support autonomy and self-determination, respect patients' wishes, and believe patients are the decision makers. However, in practice, respondents respect autonomy and self-determination to a point. Third, a model, The Patient's Decision in Discharge Planning: A Continuum, is presented where a safe discharge plan is at one end of a continuum, while an unsafe discharge plan is at the other end. Respondents respect personal autonomy and the patient's self-determination to a point. This point is likely to be located in a gray area where the patient's decision crosses from one end of the continuum to the other. When patients decide on an unsafe discharge plan, workers' interventions range from autonomy to paternalism. And fourth, the hospital setting as an outside influence may not offer the best opportunity for patients to make decisions (a) because of beliefs family members and health care professionals hold about the value of patient self-determination, and (b) because patients may not feel free to make decisions in an environment where they are surrounded by family members, health care professionals, and social work respondents who have power and who think they know best. Workers need to continue to educate elderly patients about their right to self-determination in the hospital setting. ^

Effect of Hydrologic Restoration on the Habitat of The Cape Sable Seaside Sparrow, Annual Report of 2003-2004

Following on our previous year’s work on ‘Effect of hydrologic restoration on the habitat of the Cape Sable seaside sparrow (CSSS)’, we presented first year results at the Cape Sable seaside sparrow – fire planning workshop at Everglades National Park in December 2003. Later, with almost the same set of crews as in the previous year, we started field work in the first week of January and continued till May 26, 2004. Protocols for sampling topography and vegetation in 2004 were identical to the previous year. In the early season, we completed topographic surveys along two remaining transects, B and E (~16.5 km), and vegetation surveys along three transects, D, E and F (~10.8 km), leaving only the vegetation sampling on transects B and C to be completed in 2005. During April and May, vegetation sampling was completed at 230 census sites, making the total of 409 CSSS census sites for which we have complete vegetation data. We updated data sets from both 2003 and 2004, and analyzed them together using cluster analysis, ordination, weighted-averaging regression and analysis of variance, as we had in 2003. Additionally, we used logistic regression to examine the effect of vegetation structural parameters on the recent occurrence of CSSS. We also analyzed vegetation observations recorded by the sparrow census team in 1981 and annually between 1992 and 2004 to assess historical patterns of vegetation change in CSSS habitat.

Effect of Hydrologic Restoration on the Habitat of The Cape Sable Seaside Sparrow, Annual Report of 2004-2005

The major activities in Year 3 on ‘Effect of hydrologic restoration on the habitat of the Cape Sable seaside sparrow (CSSS)’ included presentations, field work, data analysis, and report preparation. During this period, we made 4 presentations, two at the CSSS – fire planning workshops at Everglades National Park (ENP), one at the Society of Wetland Scientists’ meeting in Charleston, SC, and a fourth at the Marl Prairie/CSSS performance measure workshop at ENP. We started field work in the third week of January and continued till June 3, 2005. Early in the field season, we completed vegetation surveys along two transects, B and C (~15.1 km). During April and May, vegetation sampling was completed at 199 census sites, bringing to 608 the total number of CSSS census sites with quantitative vegetation data. We updated data sets from all three years, 2003-05, and analyzed them using cluster analysis and ordination as in previous two years. However, instead of weighted averaging, we used weighted-averaging partial least square regression (WA-PLS) model, as this method is considered an improvement over WA for inferring values of environmental variables from biological species composition. We also validated the predictive power of the WA-PLS regression model by applying it to a sub-set of 100 census sites for which hydroperiods were “known” from two sources, i.e., from elevations calculated from concurrent water depth measurements onsite and at nearby water level recorders, and from USGS digital elevation data. Additionally, we collected biomass samples at 88 census sites, and determined live and dead aboveground plant biomass. Using vegetation structure and biomass data from those sites, we developed a regression model that we used to predict aboveground biomass at all transects and census sites. Finally, biomass data was analyzed in relation to hydroperiod and fire frequency.