Program Domains of Trauma-Informed Practice Associated with Client Perception of Care and Staff Perceived Organizational Support: A Study of Women’s Substance Use Programs Utilizing Qualitative Comparative Analysis
Bibliographic record
Abstract
Background: Trauma-informed practice (TIP) is a strength-based service delivery approach that acknowledges the widespread prevalence and impact of trauma in the lives of both the people seeking services as well as amongst its’ workforce (Harris Fallot, 2001; SAMHSA, 2014). As programs and organizations implement a TIP approach to service delivery, examining the degree to which an organization is able to integrate TIP principles into their practices and operations and how it relates to client and staff perception of care can be helpful for program evaluation. Purpose Objectives: Guided by a Knowledge Based View (KBV) of the firm (Grant, 1997; 2002; 1991) and Resource Based Theory (RBT) (Barney, 1991), this study sought to examine combinations of program domains and the degree to which they are trauma-informed that are associated with client perception of care and staff perceived organizational support. Methods: A case study design involving nine women’s substance use agencies across the province of Ontario, Canada was employed to study TIP organizational capability in six program domains. To facilitate the study of combinations of organizational capability domains that are associated with the outcomes, Qualitative Comparative Analysis (QCA) was used as the main analytic approach. Findings: QCA results indicate, that the degree to which program domains are trauma-informed may not be related to staff perceived organizational support and client perception of care outcomes as anticipated. It is possible that organizational capability in TIP has more distal and perhaps little connection to the outcomes as measured by the SPOS and OPOC. Conclusions: Although the CCTIC has utility to inform program planning and implementation for trauma-informed services, it may be challenged as a tool to evaluate the extent to which program domains are associated with client perception of care and staff perceived support. Future studies should look at replicating the CCTIC as a more refined and calibrated measure of TIP that can be readily used to evaluate TIP as it relates to provider and client experiences of care to inform policy and program evaluation.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".