Faculty Profiles

Richard Francoeur

Professor
School of Social Work

Social Work Building 145
516.877.4337
francoeur@adelphi.edu
https://​www.​researchgate.​net/​profile/​Richard_Francoeur

General Information

Personal Statement

Personal Statement

Background

My odyssey as a medical social worker in the VA Pittsburgh Healthcare System stimulates and informs my research, scholarship, and teaching on physical and mental health symptoms in specific or coexisting health conditions; coping with illness by middle-age/older adults; chronic care; palliative care; social work direct practice (including evidence-based and organizational); and program development and evaluation.

My published research on co-occurrences focuses on coexisting medical conditions (or multimorbidities), clinically relevant depression/depressive symptoms, physical symptoms, and patient reactions.  This quest calls for deriving new types of knowledge and spurs me to create innovations in statistical modeling—which, to date, sharpen the detection of interactions, facilitate interpretation of their synergies, unveil elusive or unbiased psychometric subgroups, and improve data mining of homogeneous subsamples (clusters).  I am developing a protocol for studies of personalized medicine to reveal promising clinical targets by using a more thorough means to adjust confounding when assessing symptom, biomarker, or metabolite panels in main or interactive epidemiological contexts (diagnoses, genes, epigenetics, environmental or other risks).

I pursue scholarship on social work roles in health care.  Over the past few decades, I have generated, developed, and published ideas for novel or improved program components, and programs, in several areas.  These include contributions to increase inner-city and rural access to health care and palliative care; ensure safe access to pain medications while preventing prescription drug abuse; integrate spirituality into advance care planning; monitor hypertension in the community; screen hidden depression in minority men receiving palliative care using a single item that incorporates uncertain and missing responses; reformulate financial problems and interventions to improve psychosocial and functional outcomes; and meet needs (mental health, biopsychosocial, alcohol rehabilitation) of medical patients experiencing poverty.

Research Projects and Grants

I completed several research projects mostly on my own, such as 1) symptom clusters of late-life "depression without sadness" (presenting with low dysphoric—yet also low positive—affect) in progressive vascular disease (hypertensive, arterial, cardiac, cerebral), including with excess weight and/or diabetes (metabolic syndromes); 2) cancer neuroimmune symptom interactions (related to cytokines—signaling cell proteins—that induce inflammation) and comorbid "sickness malaise" (a broad index from items that tap negative affect—distress, depression—or low positive affect); and 3) financial strain as age-based reactions by cancer patients to specific and cumulative family financial stress. 

As part of a new protocol for studies of personalized medicine (see above), I am extending the first of these projects to explore symptom clusters of late-life depression in congestive heart failure, with multimorbidity from diabetes and vascular conditions. 

I also plan to reassess meta-analyses of breast cancer support groups to improve symptoms and survival.

I attracted more than a quarter million dollars in funding (direct costs) as principal investigator of a National Institute of Mental Health grant (R03) on late-life vascular depression, a Hartford Geriatric Social Work Faculty Scholar Award, and an Open Society Social Work Leadership Award (Project on Death in America).

Innovations in Statistical Modeling

♦ Currently, I am leading a project with an Adelphi computer science alumnus to build an easy-to-use web application that will execute algorithms I developed (see below) to probe effects of interacting variables. 

As stated previously, I am developing a new protocol for studies of personalized medicine to reveal promising clinical targets by using a more thorough means to adjust confounding when assessing a panel of symptoms, biomarkers, or metabolites in main or interactive epidemiological contexts (diagnoses, genes, epigenetics, environmental or other risks).  It will provide guidance for unveiling unbiased clusters of psychometric items (the individual symptoms, biomarkers, or metabolites) of a latent trait (the overall level of the panel of items) within these main or interactive contexts.

I am also honing a way to adjust bias in meta-analysis in order to derive more credible meta-analyses of clinical practices and programs when the evidence is based on heterogeneous participants, study conditions, interventions, and/or research methods.  These more valid findings are important in the process of justifying and adopting sound evidence-based practices, programs, and policies.

♦ Recently, I forged data-mining of heterogeneous samples in multiple regression to derive homogeneous subgroups with similar effects, by adjusting heteroscedasticity, multicollinearity, and missing variables.

♦ Previously, I created or extended ways to probe models based on multiple regression for co-occurrence, including comorbidity and multimorbidity, and for clustering of items or symptoms, namely to:

• Detect interactions in moderated regression (MR) more sensitively and precisely (with lower standard errors, tighter confidence intervals);

• Interpret quickly the nature (magnifying and/or buffering), strength, and statistical significance of MR synergies across discrete values of interacting variables (this advance avoids the need to respecify predictors at different levels, re-estimate MR repeatedly, and construct multiple graphs; app pending); and

• Unveil elusive clusters of psychometric items (or symptoms) of a latent trait, either broadly (across a group) or uniquely (within an MR-targeted subgroup)

The advance in the last bullet affords insight into distinct presentations, even phenomenologies, tapped by a latent trait that occur across a group (determined by a predictor), or synergistically, within a subgroup (determined by interacting predictors).  It overcomes the potential for common, insidious confounding in regression-based multiple indicators-multiple causes (MIMIC) models, which can estimate direct (unique) effects of predictors to the latent trait and to all but one of its items or symptoms. Even if it seems justified not to specify the direct effect to a certain item (i.e. it is fixed usually at zero), hidden bias may infect the latent trait and proliferate across items, undermining the validity of specified (shared and direct) effects. 

The advance avoids this difficulty by offering a new way to specify a MIMIC model that enables the direct effect on every single item or symptom of a scale or subscale to be unveiled (while still adjusting shared effects across, and possibly between, the items or symptoms to account for the level of the latent trait).  It reveals the subset of items or symptoms that have statistically significant direct effects, which comprise the item or symptom cluster within the group or subgroup.

Teaching Interests

• Social work practice; social work in chronic, palliative, and end-of-life care

• Evidence-based practice; client monitoring with single-case designs

• Organizational context of practice; social enterprises by social workers

• Program development and evaluation; systematic reviews; meta-analysis

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