"Ticky Box" Practice: Client Centred versus Document Centred Social Work
Bibliographic record
Abstract
Social work like many other healthcare professions seems to be moving away from client focused care to document centred care in an attempt to meet increasing assessment and accountability requirements. It may be argued that as healthcare has come to be viewed more and more as a business in the current neo-liberal climate, managerialist concepts have become entrenched in healthcare. In October 2005 the Ministry of Health and Long Term Care mandated the implementation of the Resident Assessment Instrument-Mental Health (RAI-MH) for all inpatient mental health beds in Ontario. The tool was hailed as a comprehensive assessment tool that would decrease documentation requirements for the healthcare team, as well as provide the Ministry with a means to develop a case-mix based payment system for inpatient mental health services. This qualitative research explored the origins of Minimum Data Set tools such as the RAI-MH, as well as the opinions and experiences of mental health social workers two years after the RAI-MH was implemented. Six social workers were interviewed and their practice experience ranged from new graduate to seasoned social worker. All the social workers reported increasing documentation requirements has led to less time being spent on patient care. All of them indicated that the RAI-MH has not decreased documentation but rather was added to existing documentation requirements. The newly graduated social workers had a more positive response to the RAI-MH than the seasoned social workers who appeared more skeptical. They were also more likely to report that the style of their initial interview was driven by the questions asked on the RAI-MH whereas the seasoned social workers attempted to limit its impact. The study concludes with a discussion of the implications of these findings on patient care, critical analysis as well as social work education. Possible research directions for the future are also highlighted.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".