Relationship between staffing ratios and effectiveness of inpatient psychiatric units.
OBJECTIVE
Most staffing models designed for adult psychiatric hospitals are based on the well-known relationship between high staff-patient ratios and high effectiveness of treatment units. Staffing ratios likely gain their predictive power by serving as a proxy measure for the amount of attention patients receive from staff. This study directly measured the amount of attention that patients received from staff to determine whether it could account for the variance in unit effectiveness predicted by staffing ratios and serve as a better predictor of unit effectiveness.


METHODS
Data from 22 wards in state or Department of Veterans Affairs mental health institutions, which housed 673 short- and long-stay patients, were analyzed. Only full-time direct care staff were included in the calculation of staff-patient ratios. The amount of attention provided to patients by staff was determined over seven days by a highly reliable observational assessment system. The two best social-action outcome measures over a six-month period were adjusted for confounding variables to provide residualized indexes of unit effectiveness.


RESULTS
Staff-patient ratios significantly predicted unit effectiveness as indexed by residualized community tenure, accounting for 24 percent of the variance. However, staff-patient ratios did not significantly predict unit effectiveness as indexed by net gain in discharge rates. Taking into account the amount of staff attention received by patients significantly improved the prediction of community tenure by 36 percent and the net gain in discharge rates by 66 percent over staffing ratios alone. The amount of staff attention also accounted for the relationship between staffing ratios and community tenure.


CONCLUSIONS
Staffing decisions should be made on the basis of more precise information about treatment requirements, not just staffing ratios.
Coleman
J C
JC
Department of Psychology, University of Houston, Houston, TX 77204-5022, USA.
Paul
G L
GL
D000328:Adult; D005260:Female; D006787:Hospitals, Veterans / Q000706:statistics & numerical data; D006801:Humans; D007087:Illinois; D008297:Male; D001523:Mental Disorders / Q000523:psychology / Q000628:therapy; D010043:Outcome and Process Assessment, Health Care; D010348:Patient Care Team / Q000706:statistics & numerical data*; D010351:Patient Discharge / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data; D011369:Professional-Patient Relations; D011567:Psychiatric Department, Hospital; D012917:Social Adjustment; D000078329:Workforce