Incorporation of trauma-informed practice principles by nurse practitioners into telemental health care provision
Bibliographic record
Abstract
A trauma-informed approach to care incorporates awareness of the how trauma, a highly prevalent experience, impacts multiple aspects of health and influences an individual’s experience of health care. Nurse practitioners working within the primary care context frequently care for complex patients with a high burden of trauma and negative past experiences with the healthcare system. It is essential that nurse practitioners are equipped to provide care that is trauma-informed, regardless of its mode of delivery. Though some trauma-informed care guidelines are available, no specific guideline is available to guide its application specific to telemental health care. Therefore, the goal of this project was to explore how nurse practitioners can incorporate the principles of trauma-informed care into their provision of telemental health care. An integrative literature review was conducted, using the framework of the Substance Abuse and Mental Health Services’ model of trauma-informed care to guide keyword selection. The initial search yielded 1028 results; after application of inclusion and exclusion criteria, a total of 15 articles were included in the review. Data relevant to the research question was extracted and collated, and the following areas were summarized: how trauma was conceptualized, how telemental health care was delivered and integrated into larger systems of care, and how each of the assumptions and principles of trauma-informed practice were demonstrated. From these findings, intrinsic characteristics of telemental health care were identified, and a model of trauma-informed telemental health care specific to the scope and context of family nurse practitioners in primary care. Recommendations for practice were made based on how nurse practitioners can capitalize on those characteristics of telemental health care that align with a trauma informed approach, including the option to avoid entering unsafe physical health care spaces, reduction of number of people in the room during vulnerable procedures, inclusion of family, friends, and community, adaptability to patient needs and preferences, access to a more diverse selection of services, and convenience. Further recommendations for practice were made to guide nurse practitioners in mitigating the risks associated with those characteristics of telemental health care that challenge the provision of trauma-informed care, including physical separation from the patient, need for patients to place an additional layer of trust in providers to take appropriate security precautions, loss of a private space provided by in-person appointments, risk of triggering and recreating traumatic experiences that involved technology, and inaccessibility of services based on their technology-related knowledge and material requirements. Finally, recommendations were made for how policy and research can address gaps in knowledge and actions required that fall beyond the scope of individual practitioners. This review suggests that while nurse practitioners cannot use telehealth as the sole vehicle through which they offer their patients trauma-informed mental health care, the principles of trauma-informed care can be incorporated into telemental health care in ways that enhance the nurse practitioner’s ability to avoid retriggering trauma, support the establishment of trust in patient-provider relationships, and take steps towards overcoming trauma-related health inequity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.099 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".