TREC Measurement System

What is the TREC Measurement System?

The TREC Measurement System (TMS) is a platform technology that enables us to leverage data for intervention studies, facility and regional level feedback, pilot studies, system projects, trainee projects and to form working groups to tackle specific questions. It is the largest longitudinal database of its kind in Canada and the only one collecting data at the unit (microsystem) level.

Using the longitudinal data from the TREC Measurement System we can explore the strength of linkages between work environment, best practice use and resident outcomes using our staff survey data and the Resident Assessment Instrument – Minimum Data set 2.0 (RAI-MDS 2.0) data. The study collects data from over 90 nursing homes across British Columbia, Alberta and Manitoba. We have a representative sample of nursing homes that includes facilities of different sizes and owner-operator models (public, voluntary and private).

Quick Facts

British Columbia

Logomakr_6fb18l.pngLength of Project
2007 to 2020 (longer pending funding)

Logomakr_1NbR52.pngFacilities involved
94 long term care facilities,
randomly chosen sample

Logomakr_172HmD.pngData Collection
Staff surveys are collected in waves, spaced at 18-24 month intervals

What is involved?

Using the TREC Survey we collect data from long term care facilities in waves spaced approximately 18 months apart. We collect information on facilities and units, and regulated and unregulated staff. Participating facilities play an important role in building the first longitudinal database on long term care in Canada. We work closely with Facility Managers, Administrators and Directors of Care to ensure that these data are collected with minimal impact on resident care.

Why is it important?

The TMS database is a rich data source that is used to provide evidence contributing to system level changes being implemented.

A unique aspect of the TREC Measurement System is its definition and use of the clinical microsystem (i.e., working units) within nursing homes. This level of focus and analysis allows us to demonstrate the benefit of targeting change at the microsystem level as well as the broader levels of care (facility, region, etc.).

We combine this data with RAI-MDS 2.0 outcome data, and conduct further analyses to identify factors that affect resident outcomes. This provides us, and the facilities in our sample,with new knowledge to improve resident quality of care and quality of life.

Benefits to the participating facilities

  • Access to a rich source of data that links context, staff and resident data
  • Individualized feedback reports for sites and different levels of staff
  • Opportunity to share and discuss findings and strategies at Regional Summits
  • Participation in projects aligned with regional and provincial priorities
  • Opportunity for involvement in other TREC projects such as SCOPE & INFORM

Progress update: Where are we now?

We collected two rounds of data from over 4,000 care aides, 1,000 regulated staff, unit/facility management and 40,000 residents. Wave 1 data was collected between September 2014 – May 2015 and Wave 2 data was collected between May 2017 – December 2017. 

We currently launching our third wave of data collection. We will continue to work closely with facility management and care staff to ensure our data is collected with minimal impact on resident care.

We will analyze staff data for important relationships among modifiable elements of work environments, best practice use, and staff health/well-being. Staff data will be combined with RAI-MDS 2.0 data to identify factors that affect resident outcomes and will provide your facility with new knowledge to improve resident quality of care and quality of life.

We look forward to working with the facilities and care staff to understand and improve the long-term care sector.


Hoben, M., Knopp-Sihota, J.A., Nesari, M., Chamberlain, S.A., Squires, J.E., Norton, P.G., Cummings, G.G., Stevens, B.J., Estabrooks, C.A. Health of health care workers in Canadian nursing homes and pediatric hospitals: a cross-sectional study. CMAJ Open, 5(4), E791-E799.

Chamberlain, S., Gruneir, A., Hoben, M., Squires, J., Cummings, G., Estabrooks, C.A. (2017). Influence of organizational context on nursing home staff burnout: A cross-sectional survey of care aides in western Canada. International Journal of Nursing Studies, 71, 60-69.

Thompson, G.N., Doupe, M., Reid, R.C., Baumbusch, J., Estabrooks, C.A. (2017). Pain trajectories of nursing home residents nearing death. Journal of the American Medical Directors Association, 18(8), 700-706.

Chamberlain, S., Hoben, M., Squires, J., Estabrooks, C.A. (2016). Individual and organizational predictors of health care aide job satisfaction. BMC Health Services Research 16:577.

Estabrooks, C.A., Knopp-Sihota, J., Cummings, G.G., Norton, P.G. (2016). Making research results relevant and useable: Presenting complex organizational context data to non-research stakeholders in the nursing home setting. Worldviews on Evidenced-based Nursing, 13(4), 270–276.

Estabrooks, C.A., Squires, J.E., Hayduk, L., Morgan, D., Cummings, G.G., Ginsburg, L., Stewart, N., McGilton, K., Kang, S.H., Norton, P.G. (2015). The influence of organizational context on best practice use by care aides in residential long-term care settings. Journal of the American Medical Directors Association, 16(6), 537.e1–537.e10

Estabrooks, C.A., Hoben, M., Poss, J.W., Chamberlain, S.A., Thompson, G.N., Silvius, J.L., Norton, P.G. (2015). Dying in a nursing home: Treatable symptom burden and its link to modifiable features of work context. Journal of the American Medical Directors Association, 16(6), 515–520.

Estabrooks, C.A., Squires, J.E., Carleton, H.L., Cummings, G.G., Norton, P.G. (2015). Who is looking after Mom and Dad? Unregulated workers in Canadian nursing homes. Canadian Journal on Aging, 34(1), 47-59.

Norton, P.G., Murray, M., Doupe, M.B., Cummings, G.G., Poss, J.W., Squires, J.E., Teare, G.F., Estabrooks, C.A. (2014). Facility versus unit level reporting of quality indicators in nursing homes when performance monitoring is the goal. BMJ Open, 4:e004488.

Estabrooks, C.A., Knopp-Sihota, J., Norton, P.G. (2013). Practice sensitive quality indicators in MDS-RAI 2.0 nursing home data. BMC Research Notes, 6:460.

Estabrooks, C.A., Poss, J.W., Squires, J.E., Teare, G.F., Morgan, D.G., Stewart, D., Doupe, M.B., Cummings, G.G., Norton, P.G. (2013). A profile of residents in Prairie nursing homes. Canadian Journal on Aging, 32(3), 223-231.

Boström, A.-M., Cranley, L.A., Hutchinson, A.M., Cummings, G.G., Norton, P., Estabrooks, C.A. (2012). Nursing home administrators’ perspectives on a study feedback report: A cross sectional survey. Implementation Science, 7:88.

Hutchinson, A.M., Batra-Garga, N., Cranley, L.A., Boström, A.-M., Cummings, G.G., Norton, P.G., Estabrooks, C.A. (2012). Feedback reporting of survey data to healthcare aides. Implementation Science, 7:89.

Cranley, L.A., Birdsell, J., Norton, P.G., Morgan, D.G., Estabrooks. C.A. (2012). Insights into the impact and use of research results in a residential long-term care facility: A case study. Implementation Science, 7:90.

Estabrooks, C.A., Teare, G., Norton, P.G. Should we feed back research results in the midst of a study? Implementation Science, 7:87.

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