Detector description 1

signal_detectors/Comparison_detect.md → signal_detectors/comparison_detect.md

@@ -1,6 +1,6 @@
 # Comparison Signal Detector
 ## Concept
-The comparison signal detector function compares the performance level of a feedback recipient for a specfic measure against the level of a benchmark, such as the peer performance benchmarks or the goal value for that particular measure. This function evaluates differently for both types of gaps, both positive and negative, relative to the performance level and the list of comparator values that are pre-defined by the pipeline. When the recipient's performance level is above that of a comparator, the comparison _detect method denotes that there exists a positive gap in performance level, which is a kind of motivating information. When this detector is activated, the positive or negative gap motivating information is used to influence the rank of corresponding precision feedback message templates.
+The comparison signal detector function compares the performance level of a feedback recipient for a specfic measure against the level of a comparator, such as a peer-based benchmark or a goal set by an organization. This function evaluates differently for both types of gaps, both positive and negative, relative to the performance level and the list of comparator values that are pre-defined by the pipeline. When the recipient's performance level is above that of a comparator, the comparison _detect method denotes that there exists a positive gap in performance level, which is a kind of motivating information. When this detector is activated, the positive or negative gap motivating information is used to influence the rank of corresponding precision feedback message templates.
 
 ## Example
 Below are examples of performance data that leads to the detection of a comparison signal.

signal_detectors/trend_detect.md

@@ -0,0 +1,73 @@
+# Trend Detector
+
+## Introduction
+The trend detector function identifies changes in the performance level of a feedback recipient for a single measure or metric across time intervals. This function interprets trends as changes in the absolute rate of performance. 
+
+The trend detector function receives performance data as input, and produces the following outputs:
+1. Identification: Indicates the existence of a trend
+2. Monotonicity: Classifies a trend as simple monotonic or nonmonotonic
+3. Slope: 
+	- Classifies the slope as negative or positive
+	- Indicates magnitude of the trend (Range: 0 to 1)
+4. Duration: Indicates the number of time intervals (i.e. months) that the trend spans
+
+The output of the trend detector function is used by a precision feedback system to prioritize candidate messages.
+
+## Example
+The performance data below contains an example trend that can be detected by the Trend Signal Detector function:
+
+|Month|Performance Level|Peer Average|75th Percentile Benchmark|90th Percentile Benchmark|Organizational Goal|
+|-----|-----------------|------------|-------------------------|-------------------------|--|
+|Oct  |       85%| 85| 88| 92|90|
+|Nov  | ***85%***| 85| 88| 92|90|
+|Dec  | ***91%***| 85| 88| 92|90|
+
+- For the month of December, this performance data will generate a positive trend signal. The magnitude of the trend will be evaluated as 3, the formula below denotes how this magnitude is determined:
+     - trend magnitude = (month t0 - month t2)/2
+     - trend magnitude = **(91 - 85)/2 = 3**
+
+## Implementation
+The method operates with the following formula:
+time1 performance - time2 performance / 2
+
+## Signal detection criteria
+The following criteria must be met for a trend to be detected:
+
+1. The recipient's performance level must differ between the current month and the previous month.
+
+
+
+## Test cases
+These test cases are based on the following performance dataset header:
+
+      ["staff_number","measure","month","passed_count","flagged_count","denominator","peer_average_comparator","peer_75th_percentile_benchmark","peer_90th_percentile_benchmark", "MPOG_goal"],
+
+1. A trend is detected in these cases:
+
+      [3,"PUL01","2023-01-01",1,0,0,1,2,9,90],
+      [3,"PUL01","2023-02-01",40,0,0,40,42,6,90],
+      [3,"PUL01","2023-03-01",100,0,0,100,33,99,90],
+
+      [3,"PUL01","2023-01-01",99,0,0,18,2,9,90],
+      [3,"PUL01","2023-02-01",98,0,0,48,42,6,90],
+      [3,"PUL01","2023-03-01",97,0,0,1,33,99,90],
+
+      [3,"PUL01","2024-02-01",78,0,0,100,88,9,90],
+      [3,"PUL01","2024-03-01",18,0,0,40,42,68,90],
+      [3,"PUL01","2024-04-01",17,0,0,100,33,99,90],
+
+
+2. No trend is detected in these cases:
+
+      [3,"PUL01","2023-01-01",1,0,0,1,22,19,90],
+      [3,"PUL01","2023-02-01",1,0,0,40,2,76,90],
+      [3,"PUL01","2023-03-01",1,0,0,10,33,9,90],
+
+      [3,"PUL01","2023-01-01",99,0,0,1,2,9,90],
+      [3,"PUL01","2023-02-01",99,0,0,40,42,6,90],
+      [3,"PUL01","2023-03-01",99,0,0,100,33,99,90],
+
+      [3,"PUL01","2024-02-01",78,0,0,1,2,9,90],
+      [3,"PUL01","2024-03-01",78,0,0,40,42,6,90],
+      [3,"PUL01","2024-04-01",78,0,0,100,33,99,90],
+