Exploring self-reflection in dual relationship decision-making
Bibliographic record
Abstract
Summary Social workers practicing in small rural towns regularly encounter overlapping or dual relationships with clients. This can lead to boundary crossing and conflicts of interest that require an ethical decision. Previous research on how rural social workers approach ethical decision-making in these situations has suggested that while they might draw on ethical codes, many rely on intuitive or personal viewpoints rather than systematic decision-making processes. Although social workers are often trained to engage in self-reflection when faced with the complexities of practice and the possibilities of personal biases, the role of systematic self-reflection in personal decision-making about dual relationships has not been sufficiently documented in the literature. Using qualitative inquiry, this study explored the lived experiences with dual relationships reported by 44 practitioners working in small towns in rural Canada. Findings The findings confirm that practitioners were frequently guided in their decisions by an intuitive or emotional understanding of dual relationships rather than by external codes, and while self-reflection was seen to play a role, a formal or systematic approach to personal self-reflection was not reported. Applications The findings suggest that educational or professional development on more intentional use of self-reflection in practice would aid in safeguarding the client and the worker when dealing with dual relationships, while providing an ethical process congruent with a rural and remote community identity. Explicit decision-making would also make the process more defensible and justifiable, not only to themselves, but also to colleagues, supervisors, and to their professional association.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".