This study and its analytic approach open a dialogue about healthcare providers' motivation to screen and subsequently treat/refer women experiencing perinatal depression. Our study found that two constructs, importance and confidence, predicted providers' implementation of perinatal depression screening and treatment/referral. As shown in the model, these constructs work in tandem to influence desirable outcomes. The fit of the model suggests that motivational interviewing, assessed through its program theory components of importance and confidence, may be useful as a provider-focused intervention strategy to enhance screening and treatment/referral outcomes statewide. These results provide an evidence-based foundation on which to consider innovative training strategies that build provider confidence and enhance perceived importance of prioritizing perinatal depression screening and treatment in routine practice.
Before this study, the statewide service infrastructure had been enhanced through training, instrument availability, and reimbursement to facilitate the desirable outcomes of perinatal depression screening and treatment/referral. With this preexisting service infrastructure in place, this study's findings assert that further augmentation to services is possible by altering provider importance and confidence, as suggested by motivational interviewing as an intervention model. At present, we are moving forward with the findings from this study to consider the specific ways in which motivational interviewing can serve as both a strategy to motivate providers to screen and treat/refer and provide a technique applicable to women with perinatal depression encountered in their practice.
One specific future initiative will be to partner regionally and build confidence among providers regarding service availability options by creating a more fluid connection between available support resources for women experiencing perinatal depression. We intend to move forward by defining and categorizing the multitiered interventions available (including supportive home visiting, psychoeducation, and peer support, as well as specialty mental health programs) in order to facilitate referrals based on symptoms, severity, and service preferences. Motivational interviewing simultaneously offers a framework for a brief engagement to treatment intervention that can be used by these same health providers in their community practice. Statewide trainings to augment provider confidence will also focus on these motivational interviewing skills for use with patients who are ambivalent about help seeking for perinatal depression.
As a training technique, motivational interviewing can also be used to help providers identify their own motivations for (and ambivalence about) engaging in screening and intervention, which in turn may help them motivate clients within their particular geographic regions and service settings. Ultimately, the program theory implied by motivational interviewing offers a framework from which we can address existing ambivalence within both healthcare providers and consumers, creating meaningful pathways for collaborative intervention development.
The theoretical and statistical modeling framework of our study may help address the concerns previously encountered in other widespread screening initiatives where an action step (i.e., mandatory screening) preceded a theory of change.10,11
Notably, the empirical support for motivational interviewing generated through this study makes a distinct contribution by defining the causal mechanisms through which we assert the desirable change is taking place. Articulating causal pathways enhances our ability to understand how and why particular strategies work, how they can be measured, and what is the contribution of the strategy to proximal outcomes (e.g., augmenting importance and confidence of providers) as well as ultimate outcomes (e.g., enhanced perinatal depression screening and intervention). Theoretically informing an intervention lends support to the process of change as well as the resulting outcomes, which helps us better understand breakdowns in program efficacy so that needed interventions can be adapted by altering the specific components through which we assert that change is taking place.
There are a number of limitations to this study. First, the survey data were not originally collected with the intention of statistical modeling. Inherent measurement error, such as social desirability and participant response set patterns, may have influenced the quality of the data and measurement accuracy, ultimately reducing model fit and strength. The inability to assume conditional independence is a limitation in both the measurement model and the structural model. Attention to measurement rigor and consistency across subsequent evaluations statewide is crucial to assessing the intervention model's true effect.
A second major limitation is the inability to detect differences based on provider specialization in the structural model, given the small size of specific provider specialty subgroups. For example, only 72 respondents who had ever managed (diagnosed or treated) a case of perinatal depression were pediatricians; consequently, we lacked sufficient power to consider multigroup SEM in spite of the fact that descriptive data suggested different patterns of perceptions, screening, and treatment practices for this group. An additional limitation of this and other model identification procedures is that there may be additional explanatory models of behavioral change that would be theoretically plausible or even superior if they could be empirically tested. We were, for example, unable to measure or evaluate specific learning styles or didactic techniques that may influence the retention of information and its application to practice, as we did not include such questions on the provider survey.
An additional limitation is the potential uniqueness of our sample, whether related to who ultimately responded or to state-level differences in Virginia's providers as compared with other states or countries. We consulted with the Office of Health Professions to evaluate how well our sample represented the demographic makeup of Virginia's healthcare provider workforce. However, no specific data had been collected on race, ethnicity, or gender for providers before the current biennium. Nationally, almost 70% of health providers self-report as non-Hispanic white, and approximately 71% are male.30
Our survey demographics reveal a higher percentage of women than this national norm but a similar racial composition of our overall sample. This could be accounted for in part by the selected specialties within our sampling frame, which may attract more female providers.
Similarly, we cannot be certain about what motivated respondents to participate in this study. Although response rates are relatively stable among provider groups, the overall response rate of 25% suggests that many providers' experiences are not necessarily included in study results. Because this was a voluntary survey undertaken in a public health context, we had no way to incentivize providers or mandate response. It is difficult to assess the degree to which responding providers were more or less likely to have a higher degree of motivation for screening and referring women than nonresponding providers; one assumption may be that our sample reflects providers who were already more motivated, based on their participation. The variance of responses observed within the data, however, suggests that the sample included a range of responses among all those who had managed a case of perinatal depression, ranging from those who did not formally screen or treat/refer women at all to those who regularly integrated these recommended practices into routine care. As previously described, perceived barriers to screening and treatment/referral also indicate tangible (time, reimbursement) barriers as well as acknowledgment of inadequate knowledge and skills among providers. This attests to some degree of variability within respondents' motivations and actions.
In spite of these limitations, this study reflects our best attempt to garner information on current provider practices statewide. Ultimately, we see the results of this study as having impact on our state's program and policy based on those who responded and, in doing so, allowed their experiences to inform future public health policy and practice. The authors recognize that within our assumptions there could be unintentional sampling error and self-report bias based the views of those providers who chose to respond to the survey. An examination of widespread actual practices could produce different results. Future research could be conducted to compare the reported practices contained within this survey with insurance claims data, for example, to assess the degree to which reported provider actions match with actual reimbursement claims.