Health care provider’s relational approaches with people who inject drugs (PWIDs): Implications for PWIDs’ health care
Bibliographic record
Abstract
The objectives of this study were to: (i) understand HCPs’ relational approaches to providing care and services to PWIDs to whom they provide care and services to, (ii) examine the factors that influence HCPs relational approaches with PWIDs, (iii) understand the implications of HCPs’ relational approaches on PWIDs, and (iv) generate knowedge on how HCPs’ relational approaches affect the health care experiences/access of specific vulnerable populations such as: women, Indigenous women, and Indigenous peoples as a whole. The methodological approach of interpretive description was employed. Qualitative data was collected using semi structured interviews and field notes from 11 HCPs who worked with predominantly PWID clients both in the community and in a hospital setting at a health region within the Saskatchewan Health Authority. Inductive thematic analysis was conducted. The interdisciplinary generalist and integrationist approaches enabled the author to draw from knowledge across disciplines (nursing, psychology, public health, and sociology) and integrate theories and frameworks such as: theory of stigma, behavioral model of utilization, and the harm reduction philosophy for substance misuse. Findings of the study revealed that HCP participants had positive relational approaches toward clients who inject drugs, including those with multiple vulnerabilities such as: Indigenous peoples and women. Participants claimed that negative behaviors from HCPs toward IUDs/PWIDs were likely to occur more among HCPs who worked in chaotic work environments, under pressure, and with limited staff. The relational approaches of HCPs toward PWIDs are influenced by workload, family history, and educational background of HCPs. An integrated framework for facilitating a HCP-PWID relational approach for holistic care and health access for PWIDs emerged in the study. The need for deliberateness in system change, and incentives for HCPs, such as: additional job-specific and cultural competence trainings will be invaluable for HCPs. A degree of attitudinal, cultural, and institutional shift is recommended. Policy and practice integration will help to affirm the need to understand the multiple dimensions of access to health care. This study highlights the need for future research on the relational approach of HCPs with PWIDs among HCPs who provide services to other diverse client groups often in high-pressure, chaotic work situations.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".